Overview

Dataset statistics

Number of variables43
Number of observations91713
Missing cells148756
Missing cells (%)3.8%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory30.1 MiB
Average record size in memory344.0 B

Variable types

Numeric18
Categorical24
Unsupported1

Alerts

bmi is highly overall correlated with weightHigh correlation
height is highly overall correlated with genderHigh correlation
weight is highly overall correlated with bmiHigh correlation
apache_2_diagnosis is highly overall correlated with elective_surgery and 3 other fieldsHigh correlation
apache_3j_diagnosis is highly overall correlated with elective_surgery and 3 other fieldsHigh correlation
gcs_motor_apache is highly overall correlated with gcs_eyes_apache and 1 other fieldsHigh correlation
apache_4a_hospital_death_prob is highly overall correlated with apache_4a_icu_death_probHigh correlation
apache_4a_icu_death_prob is highly overall correlated with apache_4a_hospital_death_probHigh correlation
elective_surgery is highly overall correlated with apache_2_diagnosis and 3 other fieldsHigh correlation
gender is highly overall correlated with heightHigh correlation
icu_admit_source is highly overall correlated with elective_surgery and 1 other fieldsHigh correlation
apache_post_operative is highly overall correlated with apache_2_diagnosis and 3 other fieldsHigh correlation
gcs_eyes_apache is highly overall correlated with gcs_motor_apache and 1 other fieldsHigh correlation
gcs_unable_apache is highly overall correlated with gcs_motor_apache and 2 other fieldsHigh correlation
gcs_verbal_apache is highly overall correlated with gcs_unable_apache and 1 other fieldsHigh correlation
intubated_apache is highly overall correlated with ventilated_apacheHigh correlation
ventilated_apache is highly overall correlated with gcs_verbal_apache and 1 other fieldsHigh correlation
cirrhosis is highly overall correlated with hepatic_failureHigh correlation
hepatic_failure is highly overall correlated with cirrhosisHigh correlation
apache_3j_bodysystem is highly overall correlated with apache_2_diagnosis and 2 other fieldsHigh correlation
apache_2_bodysystem is highly overall correlated with apache_2_diagnosis and 2 other fieldsHigh correlation
ethnicity is highly imbalanced (55.1%)Imbalance
icu_stay_type is highly imbalanced (77.5%)Imbalance
arf_apache is highly imbalanced (81.6%)Imbalance
gcs_unable_apache is highly imbalanced (92.2%)Imbalance
aids is highly imbalanced (99.0%)Imbalance
cirrhosis is highly imbalanced (88.3%)Imbalance
hepatic_failure is highly imbalanced (90.0%)Imbalance
immunosuppression is highly imbalanced (82.5%)Imbalance
leukemia is highly imbalanced (93.9%)Imbalance
lymphoma is highly imbalanced (96.1%)Imbalance
solid_tumor_with_metastasis is highly imbalanced (85.5%)Imbalance
hospital_death is highly imbalanced (57.6%)Imbalance
age has 4228 (4.6%) missing valuesMissing
bmi has 3429 (3.7%) missing valuesMissing
ethnicity has 1395 (1.5%) missing valuesMissing
height has 1334 (1.5%) missing valuesMissing
weight has 2720 (3.0%) missing valuesMissing
apache_2_diagnosis has 1662 (1.8%) missing valuesMissing
apache_3j_diagnosis has 1101 (1.2%) missing valuesMissing
gcs_eyes_apache has 1901 (2.1%) missing valuesMissing
gcs_motor_apache has 1901 (2.1%) missing valuesMissing
gcs_unable_apache has 1037 (1.1%) missing valuesMissing
gcs_verbal_apache has 1901 (2.1%) missing valuesMissing
map_apache has 994 (1.1%) missing valuesMissing
resprate_apache has 1234 (1.3%) missing valuesMissing
temp_apache has 4108 (4.5%) missing valuesMissing
apache_4a_hospital_death_prob has 7947 (8.7%) missing valuesMissing
apache_4a_icu_death_prob has 7947 (8.7%) missing valuesMissing
apache_3j_bodysystem has 1662 (1.8%) missing valuesMissing
apache_2_bodysystem has 1662 (1.8%) missing valuesMissing
Unnamed: 83 has 91713 (100.0%) missing valuesMissing
encounter_id is uniformly distributedUniform
patient_id is uniformly distributedUniform
encounter_id has unique valuesUnique
patient_id has unique valuesUnique
Unnamed: 83 is an unsupported type, check if it needs cleaning or further analysisUnsupported
pre_icu_los_days has 3711 (4.0%) zerosZeros
apache_4a_hospital_death_prob has 2488 (2.7%) zerosZeros
apache_4a_icu_death_prob has 9694 (10.6%) zerosZeros

Reproduction

Analysis started2023-08-02 18:14:36.910710
Analysis finished2023-08-02 18:16:07.185416
Duration1 minute and 30.27 seconds
Software versionpandas-profiling v3.6.6
Download configurationconfig.json

Variables

encounter_id
Real number (ℝ)

UNIFORM  UNIQUE 

Distinct91713
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean65606.079
Minimum1
Maximum131051
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size716.6 KiB
2023-08-02T15:16:07.334051image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile6569.6
Q132852
median65665
Q398342
95-th percentile124513.8
Maximum131051
Range131050
Interquartile range (IQR)65490

Descriptive statistics

Standard deviation37795.089
Coefficient of variation (CV)0.57609125
Kurtosis-1.1979326
Mean65606.079
Median Absolute Deviation (MAD)32748
Skewness-0.0022577717
Sum6.0169303 × 109
Variance1.4284687 × 109
MonotonicityNot monotonic
2023-08-02T15:16:07.565309image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
66154 1
 
< 0.1%
76188 1
 
< 0.1%
37789 1
 
< 0.1%
108084 1
 
< 0.1%
33405 1
 
< 0.1%
54864 1
 
< 0.1%
33807 1
 
< 0.1%
77443 1
 
< 0.1%
25417 1
 
< 0.1%
56426 1
 
< 0.1%
Other values (91703) 91703
> 99.9%
ValueCountFrequency (%)
1 1
< 0.1%
3 1
< 0.1%
4 1
< 0.1%
6 1
< 0.1%
9 1
< 0.1%
11 1
< 0.1%
12 1
< 0.1%
13 1
< 0.1%
14 1
< 0.1%
15 1
< 0.1%
ValueCountFrequency (%)
131051 1
< 0.1%
131049 1
< 0.1%
131048 1
< 0.1%
131047 1
< 0.1%
131046 1
< 0.1%
131045 1
< 0.1%
131044 1
< 0.1%
131043 1
< 0.1%
131042 1
< 0.1%
131040 1
< 0.1%

patient_id
Real number (ℝ)

UNIFORM  UNIQUE 

Distinct91713
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean65537.131
Minimum1
Maximum131051
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size716.6 KiB
2023-08-02T15:16:07.785385image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile6612.8
Q132830
median65413
Q398298
95-th percentile124489.8
Maximum131051
Range131050
Interquartile range (IQR)65468

Descriptive statistics

Standard deviation37811.252
Coefficient of variation (CV)0.57694396
Kurtosis-1.198444
Mean65537.131
Median Absolute Deviation (MAD)32724
Skewness0.00077708765
Sum6.0106069 × 109
Variance1.4296908 × 109
MonotonicityNot monotonic
2023-08-02T15:16:08.013133image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
25312 1
 
< 0.1%
14664 1
 
< 0.1%
60980 1
 
< 0.1%
47973 1
 
< 0.1%
32805 1
 
< 0.1%
102085 1
 
< 0.1%
34219 1
 
< 0.1%
92475 1
 
< 0.1%
35277 1
 
< 0.1%
70293 1
 
< 0.1%
Other values (91703) 91703
> 99.9%
ValueCountFrequency (%)
1 1
< 0.1%
2 1
< 0.1%
3 1
< 0.1%
4 1
< 0.1%
5 1
< 0.1%
6 1
< 0.1%
7 1
< 0.1%
8 1
< 0.1%
9 1
< 0.1%
10 1
< 0.1%
ValueCountFrequency (%)
131051 1
< 0.1%
131049 1
< 0.1%
131048 1
< 0.1%
131047 1
< 0.1%
131045 1
< 0.1%
131044 1
< 0.1%
131043 1
< 0.1%
131041 1
< 0.1%
131040 1
< 0.1%
131035 1
< 0.1%

hospital_id
Real number (ℝ)

Distinct147
Distinct (%)0.2%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean105.66926
Minimum2
Maximum204
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size716.6 KiB
2023-08-02T15:16:08.273838image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/

Quantile statistics

Minimum2
5-th percentile14
Q147
median109
Q3161
95-th percentile196
Maximum204
Range202
Interquartile range (IQR)114

Descriptive statistics

Standard deviation62.854406
Coefficient of variation (CV)0.59482205
Kurtosis-1.3585667
Mean105.66926
Median Absolute Deviation (MAD)59
Skewness-0.045682845
Sum9691245
Variance3950.6764
MonotonicityNot monotonic
2023-08-02T15:16:08.523957image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
118 4333
 
4.7%
19 3925
 
4.3%
188 3095
 
3.4%
161 2792
 
3.0%
70 2754
 
3.0%
196 2730
 
3.0%
176 2583
 
2.8%
21 2470
 
2.7%
194 2258
 
2.5%
174 2225
 
2.4%
Other values (137) 62548
68.2%
ValueCountFrequency (%)
2 1284
1.4%
3 16
 
< 0.1%
4 7
 
< 0.1%
5 414
 
0.5%
6 238
 
0.3%
8 388
 
0.4%
9 43
 
< 0.1%
10 740
0.8%
13 1029
1.1%
14 792
0.9%
ValueCountFrequency (%)
204 1261
1.4%
202 307
 
0.3%
200 367
 
0.4%
199 546
 
0.6%
198 29
 
< 0.1%
197 127
 
0.1%
196 2730
3.0%
195 215
 
0.2%
194 2258
2.5%
192 516
 
0.6%

age
Real number (ℝ)

Distinct74
Distinct (%)0.1%
Missing4228
Missing (%)4.6%
Infinite0
Infinite (%)0.0%
Mean62.309516
Minimum16
Maximum89
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size716.6 KiB
2023-08-02T15:16:08.754080image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/

Quantile statistics

Minimum16
5-th percentile29
Q152
median65
Q375
95-th percentile86
Maximum89
Range73
Interquartile range (IQR)23

Descriptive statistics

Standard deviation16.775119
Coefficient of variation (CV)0.26922242
Kurtosis-0.21009006
Mean62.309516
Median Absolute Deviation (MAD)11
Skewness-0.6244742
Sum5451148
Variance281.40461
MonotonicityNot monotonic
2023-08-02T15:16:08.960052image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
67 2271
 
2.5%
68 2173
 
2.4%
71 2141
 
2.3%
72 2110
 
2.3%
66 2059
 
2.2%
65 2051
 
2.2%
70 2032
 
2.2%
63 1977
 
2.2%
73 1972
 
2.2%
64 1956
 
2.1%
Other values (64) 66743
72.8%
(Missing) 4228
 
4.6%
ValueCountFrequency (%)
16 44
 
< 0.1%
17 126
 
0.1%
18 259
0.3%
19 343
0.4%
20 338
0.4%
21 372
0.4%
22 389
0.4%
23 400
0.4%
24 401
0.4%
25 415
0.5%
ValueCountFrequency (%)
89 952
1.0%
88 1042
1.1%
87 1204
1.3%
86 1295
1.4%
85 1418
1.5%
84 1560
1.7%
83 1616
1.8%
82 1590
1.7%
81 1661
1.8%
80 1702
1.9%

bmi
Real number (ℝ)

HIGH CORRELATION  MISSING 

Distinct34888
Distinct (%)39.5%
Missing3429
Missing (%)3.7%
Infinite0
Infinite (%)0.0%
Mean29.185818
Minimum14.844926
Maximum67.81499
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size716.6 KiB
2023-08-02T15:16:09.191048image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/

Quantile statistics

Minimum14.844926
5-th percentile18.855359
Q123.641975
median27.654655
Q332.930206
95-th percentile44.516538
Maximum67.81499
Range52.970064
Interquartile range (IQR)9.2882304

Descriptive statistics

Standard deviation8.2751422
Coefficient of variation (CV)0.28353299
Kurtosis3.4114894
Mean29.185818
Median Absolute Deviation (MAD)4.5096967
Skewness1.440833
Sum2576640.7
Variance68.477979
MonotonicityNot monotonic
2023-08-02T15:16:09.419204image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
14.84492591 443
 
0.5%
67.81498973 422
 
0.5%
24.01776785 87
 
0.1%
24.20811 86
 
0.1%
27.35933163 82
 
0.1%
22.60610272 79
 
0.1%
23.29590458 79
 
0.1%
24.8046875 79
 
0.1%
25.81229652 77
 
0.1%
29.049732 76
 
0.1%
Other values (34878) 86774
94.6%
(Missing) 3429
 
3.7%
ValueCountFrequency (%)
14.84492591 443
0.5%
14.84526746 1
 
< 0.1%
14.86419531 1
 
< 0.1%
14.86453979 2
 
< 0.1%
14.86695021 1
 
< 0.1%
14.86737667 1
 
< 0.1%
14.87603306 1
 
< 0.1%
14.8780004 1
 
< 0.1%
14.87871348 1
 
< 0.1%
14.88002976 1
 
< 0.1%
ValueCountFrequency (%)
67.81498973 422
0.5%
67.81263563 1
 
< 0.1%
67.7978059 1
 
< 0.1%
67.78345128 1
 
< 0.1%
67.77927558 1
 
< 0.1%
67.74710784 1
 
< 0.1%
67.72486772 1
 
< 0.1%
67.71626298 1
 
< 0.1%
67.71150768 1
 
< 0.1%
67.5078125 1
 
< 0.1%

elective_surgery
Categorical

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size716.6 KiB
0
74862 
1
16851 

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters91713
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0
2nd row0
3rd row0
4th row1
5th row0

Common Values

ValueCountFrequency (%)
0 74862
81.6%
1 16851
 
18.4%

Length

2023-08-02T15:16:09.611431image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-08-02T15:16:09.864633image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
ValueCountFrequency (%)
0 74862
81.6%
1 16851
 
18.4%

Most occurring characters

ValueCountFrequency (%)
0 74862
81.6%
1 16851
 
18.4%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 91713
100.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 74862
81.6%
1 16851
 
18.4%

Most occurring scripts

ValueCountFrequency (%)
Common 91713
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0 74862
81.6%
1 16851
 
18.4%

Most occurring blocks

ValueCountFrequency (%)
ASCII 91713
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 74862
81.6%
1 16851
 
18.4%

ethnicity
Categorical

IMBALANCE  MISSING 

Distinct6
Distinct (%)< 0.1%
Missing1395
Missing (%)1.5%
Memory size716.6 KiB
Caucasian
70684 
African American
9547 
Other/Unknown
 
4374
Hispanic
 
3796
Asian
 
1129

Length

Max length16
Median length9
Mean length9.8939636
Min length5

Characters and Unicode

Total characters893603
Distinct characters25
Distinct categories4 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowCaucasian
2nd rowCaucasian
3rd rowCaucasian
4th rowCaucasian
5th rowCaucasian

Common Values

ValueCountFrequency (%)
Caucasian 70684
77.1%
African American 9547
 
10.4%
Other/Unknown 4374
 
4.8%
Hispanic 3796
 
4.1%
Asian 1129
 
1.2%
Native American 788
 
0.9%
(Missing) 1395
 
1.5%

Length

2023-08-02T15:16:10.026677image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-08-02T15:16:10.233248image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
ValueCountFrequency (%)
caucasian 70684
70.2%
american 10335
 
10.3%
african 9547
 
9.5%
other/unknown 4374
 
4.3%
hispanic 3796
 
3.8%
asian 1129
 
1.1%
native 788
 
0.8%

Most occurring characters

ValueCountFrequency (%)
a 237647
26.6%
n 108613
12.2%
i 100075
11.2%
c 94362
 
10.6%
s 75609
 
8.5%
C 70684
 
7.9%
u 70684
 
7.9%
r 24256
 
2.7%
A 21011
 
2.4%
e 15497
 
1.7%
Other values (15) 75165
 
8.4%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter 773867
86.6%
Uppercase Letter 105027
 
11.8%
Space Separator 10335
 
1.2%
Other Punctuation 4374
 
0.5%

Most frequent character per category

Lowercase Letter
ValueCountFrequency (%)
a 237647
30.7%
n 108613
14.0%
i 100075
12.9%
c 94362
 
12.2%
s 75609
 
9.8%
u 70684
 
9.1%
r 24256
 
3.1%
e 15497
 
2.0%
m 10335
 
1.3%
f 9547
 
1.2%
Other values (7) 27242
 
3.5%
Uppercase Letter
ValueCountFrequency (%)
C 70684
67.3%
A 21011
 
20.0%
O 4374
 
4.2%
U 4374
 
4.2%
H 3796
 
3.6%
N 788
 
0.8%
Space Separator
ValueCountFrequency (%)
10335
100.0%
Other Punctuation
ValueCountFrequency (%)
/ 4374
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin 878894
98.4%
Common 14709
 
1.6%

Most frequent character per script

Latin
ValueCountFrequency (%)
a 237647
27.0%
n 108613
12.4%
i 100075
11.4%
c 94362
 
10.7%
s 75609
 
8.6%
C 70684
 
8.0%
u 70684
 
8.0%
r 24256
 
2.8%
A 21011
 
2.4%
e 15497
 
1.8%
Other values (13) 60456
 
6.9%
Common
ValueCountFrequency (%)
10335
70.3%
/ 4374
29.7%

Most occurring blocks

ValueCountFrequency (%)
ASCII 893603
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
a 237647
26.6%
n 108613
12.2%
i 100075
11.2%
c 94362
 
10.6%
s 75609
 
8.5%
C 70684
 
7.9%
u 70684
 
7.9%
r 24256
 
2.7%
A 21011
 
2.4%
e 15497
 
1.7%
Other values (15) 75165
 
8.4%

gender
Categorical

Distinct2
Distinct (%)< 0.1%
Missing25
Missing (%)< 0.1%
Memory size716.6 KiB
M
49469 
F
42219 

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters91688
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowM
2nd rowF
3rd rowF
4th rowF
5th rowM

Common Values

ValueCountFrequency (%)
M 49469
53.9%
F 42219
46.0%
(Missing) 25
 
< 0.1%

Length

2023-08-02T15:16:10.427189image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-08-02T15:16:10.591336image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
ValueCountFrequency (%)
m 49469
54.0%
f 42219
46.0%

Most occurring characters

ValueCountFrequency (%)
M 49469
54.0%
F 42219
46.0%

Most occurring categories

ValueCountFrequency (%)
Uppercase Letter 91688
100.0%

Most frequent character per category

Uppercase Letter
ValueCountFrequency (%)
M 49469
54.0%
F 42219
46.0%

Most occurring scripts

ValueCountFrequency (%)
Latin 91688
100.0%

Most frequent character per script

Latin
ValueCountFrequency (%)
M 49469
54.0%
F 42219
46.0%

Most occurring blocks

ValueCountFrequency (%)
ASCII 91688
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
M 49469
54.0%
F 42219
46.0%

height
Real number (ℝ)

HIGH CORRELATION  MISSING 

Distinct401
Distinct (%)0.4%
Missing1334
Missing (%)1.5%
Infinite0
Infinite (%)0.0%
Mean169.64159
Minimum137.2
Maximum195.59
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size716.6 KiB
2023-08-02T15:16:10.749671image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/

Quantile statistics

Minimum137.2
5-th percentile152.4
Q1162.5
median170.1
Q3177.8
95-th percentile187.9
Maximum195.59
Range58.39
Interquartile range (IQR)15.3

Descriptive statistics

Standard deviation10.795378
Coefficient of variation (CV)0.06363639
Kurtosis-0.39170146
Mean169.64159
Median Absolute Deviation (MAD)7.7
Skewness-0.10145534
Sum15332037
Variance116.54019
MonotonicityNot monotonic
2023-08-02T15:16:10.948771image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
167.6 5362
 
5.8%
177.8 5289
 
5.8%
160 5238
 
5.7%
172.7 4834
 
5.3%
165.1 4772
 
5.2%
170.2 4141
 
4.5%
162.6 3890
 
4.2%
182.9 3662
 
4.0%
180.3 3555
 
3.9%
175.3 3524
 
3.8%
Other values (391) 46112
50.3%
ValueCountFrequency (%)
137.2 453
0.5%
137.5 1
 
< 0.1%
137.6 1
 
< 0.1%
138 4
 
< 0.1%
138.4 1
 
< 0.1%
139 3
 
< 0.1%
139.7 58
 
0.1%
140 12
 
< 0.1%
140.1 1
 
< 0.1%
140.8 1
 
< 0.1%
ValueCountFrequency (%)
195.59 429
0.5%
195.58 5
 
< 0.1%
195.5 29
 
< 0.1%
195.3 1
 
< 0.1%
195 21
 
< 0.1%
194.3 4
 
< 0.1%
194 9
 
< 0.1%
193.4 3
 
< 0.1%
193.04 23
 
< 0.1%
193 625
0.7%

icu_admit_source
Categorical

Distinct5
Distinct (%)< 0.1%
Missing112
Missing (%)0.1%
Memory size716.6 KiB
Accident & Emergency
54060 
Operating Room / Recovery
18713 
Floor
15611 
Other Hospital
 
2358
Other ICU
 
859

Length

Max length25
Median length20
Mean length18.207476
Min length5

Characters and Unicode

Total characters1667823
Distinct characters29
Distinct categories4 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowFloor
2nd rowFloor
3rd rowAccident & Emergency
4th rowOperating Room / Recovery
5th rowAccident & Emergency

Common Values

ValueCountFrequency (%)
Accident & Emergency 54060
58.9%
Operating Room / Recovery 18713
 
20.4%
Floor 15611
 
17.0%
Other Hospital 2358
 
2.6%
Other ICU 859
 
0.9%
(Missing) 112
 
0.1%

Length

2023-08-02T15:16:11.147338image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-08-02T15:16:11.336503image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
ValueCountFrequency (%)
72773
28.1%
accident 54060
20.9%
emergency 54060
20.9%
operating 18713
 
7.2%
room 18713
 
7.2%
recovery 18713
 
7.2%
floor 15611
 
6.0%
other 3217
 
1.2%
hospital 2358
 
0.9%
icu 859
 
0.3%

Most occurring characters

ValueCountFrequency (%)
e 221536
13.3%
c 180893
 
10.8%
167476
 
10.0%
n 126833
 
7.6%
r 110314
 
6.6%
o 89719
 
5.4%
t 78348
 
4.7%
i 75131
 
4.5%
m 72773
 
4.4%
g 72773
 
4.4%
Other values (19) 472027
28.3%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter 1239552
74.3%
Uppercase Letter 188022
 
11.3%
Space Separator 167476
 
10.0%
Other Punctuation 72773
 
4.4%

Most frequent character per category

Lowercase Letter
ValueCountFrequency (%)
e 221536
17.9%
c 180893
14.6%
n 126833
10.2%
r 110314
8.9%
o 89719
7.2%
t 78348
 
6.3%
i 75131
 
6.1%
m 72773
 
5.9%
g 72773
 
5.9%
y 72773
 
5.9%
Other values (7) 138459
11.2%
Uppercase Letter
ValueCountFrequency (%)
A 54060
28.8%
E 54060
28.8%
R 37426
19.9%
O 21930
11.7%
F 15611
 
8.3%
H 2358
 
1.3%
I 859
 
0.5%
C 859
 
0.5%
U 859
 
0.5%
Other Punctuation
ValueCountFrequency (%)
& 54060
74.3%
/ 18713
 
25.7%
Space Separator
ValueCountFrequency (%)
167476
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin 1427574
85.6%
Common 240249
 
14.4%

Most frequent character per script

Latin
ValueCountFrequency (%)
e 221536
15.5%
c 180893
12.7%
n 126833
 
8.9%
r 110314
 
7.7%
o 89719
 
6.3%
t 78348
 
5.5%
i 75131
 
5.3%
m 72773
 
5.1%
g 72773
 
5.1%
y 72773
 
5.1%
Other values (16) 326481
22.9%
Common
ValueCountFrequency (%)
167476
69.7%
& 54060
 
22.5%
/ 18713
 
7.8%

Most occurring blocks

ValueCountFrequency (%)
ASCII 1667823
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
e 221536
13.3%
c 180893
 
10.8%
167476
 
10.0%
n 126833
 
7.6%
r 110314
 
6.6%
o 89719
 
5.4%
t 78348
 
4.7%
i 75131
 
4.5%
m 72773
 
4.4%
g 72773
 
4.4%
Other values (19) 472027
28.3%

icu_id
Real number (ℝ)

Distinct241
Distinct (%)0.3%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean508.35769
Minimum82
Maximum927
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size716.6 KiB
2023-08-02T15:16:11.521715image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/

Quantile statistics

Minimum82
5-th percentile98
Q1369
median504
Q3679
95-th percentile876
Maximum927
Range845
Interquartile range (IQR)310

Descriptive statistics

Standard deviation228.98966
Coefficient of variation (CV)0.45044988
Kurtosis-0.81918394
Mean508.35769
Median Absolute Deviation (MAD)162
Skewness-0.16393995
Sum46623009
Variance52436.265
MonotonicityNot monotonic
2023-08-02T15:16:11.704080image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
646 1325
 
1.4%
653 1307
 
1.4%
876 1284
 
1.4%
413 1239
 
1.4%
236 1140
 
1.2%
337 1097
 
1.2%
133 1042
 
1.1%
434 993
 
1.1%
840 945
 
1.0%
404 944
 
1.0%
Other values (231) 80397
87.7%
ValueCountFrequency (%)
82 150
 
0.2%
83 187
 
0.2%
85 272
 
0.3%
87 234
 
0.3%
89 142
 
0.2%
90 580
0.6%
91 323
 
0.4%
92 881
1.0%
93 313
 
0.3%
95 820
0.9%
ValueCountFrequency (%)
927 199
0.2%
926 109
0.1%
925 121
0.1%
922 22
 
< 0.1%
921 247
0.3%
918 161
0.2%
917 16
 
< 0.1%
915 77
 
0.1%
909 17
 
< 0.1%
908 215
0.2%

icu_stay_type
Categorical

Distinct3
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size716.6 KiB
admit
86183 
transfer
 
4970
readmit
 
560

Length

Max length8
Median length5
Mean length5.1747844
Min length5

Characters and Unicode

Total characters474595
Distinct characters10
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowadmit
2nd rowadmit
3rd rowadmit
4th rowadmit
5th rowadmit

Common Values

ValueCountFrequency (%)
admit 86183
94.0%
transfer 4970
 
5.4%
readmit 560
 
0.6%

Length

2023-08-02T15:16:11.881560image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-08-02T15:16:12.064380image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
ValueCountFrequency (%)
admit 86183
94.0%
transfer 4970
 
5.4%
readmit 560
 
0.6%

Most occurring characters

ValueCountFrequency (%)
a 91713
19.3%
t 91713
19.3%
d 86743
18.3%
m 86743
18.3%
i 86743
18.3%
r 10500
 
2.2%
e 5530
 
1.2%
n 4970
 
1.0%
s 4970
 
1.0%
f 4970
 
1.0%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter 474595
100.0%

Most frequent character per category

Lowercase Letter
ValueCountFrequency (%)
a 91713
19.3%
t 91713
19.3%
d 86743
18.3%
m 86743
18.3%
i 86743
18.3%
r 10500
 
2.2%
e 5530
 
1.2%
n 4970
 
1.0%
s 4970
 
1.0%
f 4970
 
1.0%

Most occurring scripts

ValueCountFrequency (%)
Latin 474595
100.0%

Most frequent character per script

Latin
ValueCountFrequency (%)
a 91713
19.3%
t 91713
19.3%
d 86743
18.3%
m 86743
18.3%
i 86743
18.3%
r 10500
 
2.2%
e 5530
 
1.2%
n 4970
 
1.0%
s 4970
 
1.0%
f 4970
 
1.0%

Most occurring blocks

ValueCountFrequency (%)
ASCII 474595
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
a 91713
19.3%
t 91713
19.3%
d 86743
18.3%
m 86743
18.3%
i 86743
18.3%
r 10500
 
2.2%
e 5530
 
1.2%
n 4970
 
1.0%
s 4970
 
1.0%
f 4970
 
1.0%

icu_type
Categorical

Distinct8
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size716.6 KiB
Med-Surg ICU
50586 
MICU
7695 
Neuro ICU
7675 
CCU-CTICU
7156 
SICU
5209 
Other values (3)
13392 

Length

Max length12
Median length12
Mean length9.6795765
Min length4

Characters and Unicode

Total characters887743
Distinct characters18
Distinct categories4 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowCTICU
2nd rowMed-Surg ICU
3rd rowMed-Surg ICU
4th rowCTICU
5th rowMed-Surg ICU

Common Values

ValueCountFrequency (%)
Med-Surg ICU 50586
55.2%
MICU 7695
 
8.4%
Neuro ICU 7675
 
8.4%
CCU-CTICU 7156
 
7.8%
SICU 5209
 
5.7%
Cardiac ICU 4776
 
5.2%
CSICU 4613
 
5.0%
CTICU 4003
 
4.4%

Length

2023-08-02T15:16:12.222846image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-08-02T15:16:12.431337image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
ValueCountFrequency (%)
icu 63037
40.7%
med-surg 50586
32.7%
micu 7695
 
5.0%
neuro 7675
 
5.0%
ccu-cticu 7156
 
4.6%
sicu 5209
 
3.4%
cardiac 4776
 
3.1%
csicu 4613
 
3.0%
cticu 4003
 
2.6%

Most occurring characters

ValueCountFrequency (%)
C 126573
14.3%
U 98869
11.1%
I 91713
10.3%
r 63037
7.1%
63037
7.1%
S 60408
 
6.8%
M 58281
 
6.6%
u 58261
 
6.6%
e 58261
 
6.6%
- 57742
 
6.5%
Other values (8) 151561
17.1%

Most occurring categories

ValueCountFrequency (%)
Uppercase Letter 454678
51.2%
Lowercase Letter 312286
35.2%
Space Separator 63037
 
7.1%
Dash Punctuation 57742
 
6.5%

Most frequent character per category

Lowercase Letter
ValueCountFrequency (%)
r 63037
20.2%
u 58261
18.7%
e 58261
18.7%
d 55362
17.7%
g 50586
16.2%
a 9552
 
3.1%
o 7675
 
2.5%
i 4776
 
1.5%
c 4776
 
1.5%
Uppercase Letter
ValueCountFrequency (%)
C 126573
27.8%
U 98869
21.7%
I 91713
20.2%
S 60408
13.3%
M 58281
12.8%
T 11159
 
2.5%
N 7675
 
1.7%
Space Separator
ValueCountFrequency (%)
63037
100.0%
Dash Punctuation
ValueCountFrequency (%)
- 57742
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin 766964
86.4%
Common 120779
 
13.6%

Most frequent character per script

Latin
ValueCountFrequency (%)
C 126573
16.5%
U 98869
12.9%
I 91713
12.0%
r 63037
8.2%
S 60408
7.9%
M 58281
7.6%
u 58261
7.6%
e 58261
7.6%
d 55362
7.2%
g 50586
 
6.6%
Other values (6) 45613
 
5.9%
Common
ValueCountFrequency (%)
63037
52.2%
- 57742
47.8%

Most occurring blocks

ValueCountFrequency (%)
ASCII 887743
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
C 126573
14.3%
U 98869
11.1%
I 91713
10.3%
r 63037
7.1%
63037
7.1%
S 60408
 
6.8%
M 58281
 
6.6%
u 58261
 
6.6%
e 58261
 
6.6%
- 57742
 
6.5%
Other values (8) 151561
17.1%

pre_icu_los_days
Real number (ℝ)

Distinct9757
Distinct (%)10.6%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean0.83576605
Minimum-24.947222
Maximum159.09097
Zeros3711
Zeros (%)4.0%
Negative778
Negative (%)0.8%
Memory size716.6 KiB
2023-08-02T15:16:12.625705image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/

Quantile statistics

Minimum-24.947222
5-th percentile0.000694444
Q10.035416667
median0.13888889
Q30.40902778
95-th percentile4.3877778
Maximum159.09097
Range184.03819
Interquartile range (IQR)0.37361111

Descriptive statistics

Standard deviation2.4877562
Coefficient of variation (CV)2.9766179
Kurtosis311.73464
Mean0.83576605
Median Absolute Deviation (MAD)0.12847222
Skewness10.988615
Sum76650.612
Variance6.1889308
MonotonicityNot monotonic
2023-08-02T15:16:12.825875image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0 3711
 
4.0%
0.000694444 1403
 
1.5%
0.001388889 1011
 
1.1%
0.002083333 887
 
1.0%
0.002777778 811
 
0.9%
0.003472222 780
 
0.9%
0.004861111 718
 
0.8%
0.004166667 704
 
0.8%
0.00625 651
 
0.7%
0.005555556 619
 
0.7%
Other values (9747) 80418
87.7%
ValueCountFrequency (%)
-24.94722222 1
< 0.1%
-13.775 1
< 0.1%
-11.40972222 1
< 0.1%
-10.69375 1
< 0.1%
-6.634722222 1
< 0.1%
-6.153472222 1
< 0.1%
-5.672916667 1
< 0.1%
-5.553472222 1
< 0.1%
-5.227777778 1
< 0.1%
-4.996527778 1
< 0.1%
ValueCountFrequency (%)
159.0909722 1
< 0.1%
84.36736111 1
< 0.1%
81.80277778 1
< 0.1%
78.7625 1
< 0.1%
73.02291667 1
< 0.1%
67.02361111 1
< 0.1%
64.94861111 1
< 0.1%
63.82708333 1
< 0.1%
63.04444444 1
< 0.1%
62.64791667 1
< 0.1%

weight
Real number (ℝ)

HIGH CORRELATION  MISSING 

Distinct3409
Distinct (%)3.8%
Missing2720
Missing (%)3.0%
Infinite0
Infinite (%)0.0%
Mean84.02834
Minimum38.6
Maximum186
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size716.6 KiB
2023-08-02T15:16:13.050831image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/

Quantile statistics

Minimum38.6
5-th percentile50.3
Q166.8
median80.3
Q397.1
95-th percentile130
Maximum186
Range147.4
Interquartile range (IQR)30.3

Descriptive statistics

Standard deviation25.011497
Coefficient of variation (CV)0.29765549
Kurtosis1.8416398
Mean84.02834
Median Absolute Deviation (MAD)14.9
Skewness1.0690698
Sum7477934
Variance625.57497
MonotonicityNot monotonic
2023-08-02T15:16:13.269585image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
68 974
 
1.1%
81.6 887
 
1.0%
63.5 843
 
0.9%
90.7 778
 
0.8%
77.1 738
 
0.8%
75 562
 
0.6%
72.6 560
 
0.6%
59 542
 
0.6%
80 510
 
0.6%
83.9 506
 
0.6%
Other values (3399) 82093
89.5%
(Missing) 2720
 
3.0%
ValueCountFrequency (%)
38.6 459
0.5%
38.7 5
 
< 0.1%
38.8 6
 
< 0.1%
38.9 4
 
< 0.1%
39 33
 
< 0.1%
39.1 5
 
< 0.1%
39.2 12
 
< 0.1%
39.23 1
 
< 0.1%
39.3 9
 
< 0.1%
39.4 7
 
< 0.1%
ValueCountFrequency (%)
186 447
0.5%
185.8 1
 
< 0.1%
185.6 1
 
< 0.1%
185.5 4
 
< 0.1%
185.4 1
 
< 0.1%
185.3 1
 
< 0.1%
185.2 2
 
< 0.1%
185.1 3
 
< 0.1%
185.06 1
 
< 0.1%
185 7
 
< 0.1%

apache_2_diagnosis
Real number (ℝ)

HIGH CORRELATION  MISSING 

Distinct44
Distinct (%)< 0.1%
Missing1662
Missing (%)1.8%
Infinite0
Infinite (%)0.0%
Mean185.40174
Minimum101
Maximum308
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size716.6 KiB
2023-08-02T15:16:13.509881image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/

Quantile statistics

Minimum101
5-th percentile106
Q1113
median122
Q3301
95-th percentile307
Maximum308
Range207
Interquartile range (IQR)188

Descriptive statistics

Standard deviation86.050882
Coefficient of variation (CV)0.46413201
Kurtosis-1.5855193
Mean185.40174
Median Absolute Deviation (MAD)16
Skewness0.50741046
Sum16695612
Variance7404.7542
MonotonicityNot monotonic
2023-08-02T15:16:13.712807image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram with fixed size bins (bins=44)
ValueCountFrequency (%)
113 11740
 
12.8%
301 6807
 
7.4%
302 6702
 
7.3%
112 4350
 
4.7%
308 4114
 
4.5%
117 3923
 
4.3%
124 3906
 
4.3%
122 3768
 
4.1%
303 3329
 
3.6%
110 3206
 
3.5%
Other values (34) 38206
41.7%
ValueCountFrequency (%)
101 376
 
0.4%
102 1883
2.1%
103 291
 
0.3%
104 363
 
0.4%
105 1037
 
1.1%
106 2476
2.7%
107 197
 
0.2%
108 1177
 
1.3%
109 1071
 
1.2%
110 3206
3.5%
ValueCountFrequency (%)
308 4114
4.5%
307 1818
 
2.0%
306 638
 
0.7%
305 2275
 
2.5%
304 3104
3.4%
303 3329
3.6%
302 6702
7.3%
301 6807
7.4%
219 461
 
0.5%
218 553
 
0.6%

apache_3j_diagnosis
Real number (ℝ)

HIGH CORRELATION  MISSING 

Distinct399
Distinct (%)0.4%
Missing1101
Missing (%)1.2%
Infinite0
Infinite (%)0.0%
Mean558.21638
Minimum0.01
Maximum2201.05
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size716.6 KiB
2023-08-02T15:16:13.920369image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/

Quantile statistics

Minimum0.01
5-th percentile104.01
Q1203.01
median409.02
Q3703.03
95-th percentile1501.01
Maximum2201.05
Range2201.04
Interquartile range (IQR)500.02

Descriptive statistics

Standard deviation463.26698
Coefficient of variation (CV)0.82990576
Kurtosis0.01756256
Mean558.21638
Median Absolute Deviation (MAD)294.01
Skewness1.0133974
Sum50581102
Variance214616.3
MonotonicityNot monotonic
2023-08-02T15:16:14.124165image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
501.05 4481
 
4.9%
107.01 4350
 
4.7%
106.01 3923
 
4.3%
403.01 3789
 
4.1%
703.03 3106
 
3.4%
104.01 2956
 
3.2%
502.01 2842
 
3.1%
1207.01 2763
 
3.0%
102.01 2239
 
2.4%
702.01 2064
 
2.3%
Other values (389) 58099
63.3%
ValueCountFrequency (%)
0.01 1
 
< 0.1%
0.02 1
 
< 0.1%
0.03 1
 
< 0.1%
0.04 4
 
< 0.1%
0.06 1
 
< 0.1%
0.09 2
 
< 0.1%
0.11 15
< 0.1%
0.13 1
 
< 0.1%
0.14 5
 
< 0.1%
0.15 2
 
< 0.1%
ValueCountFrequency (%)
2201.05 61
 
0.1%
2201.04 5
 
< 0.1%
2201.03 12
 
< 0.1%
2201.02 10
 
< 0.1%
2201.01 12
 
< 0.1%
2101.03 3
 
< 0.1%
2101.01 6
 
< 0.1%
1904.01 236
0.3%
1903.03 56
 
0.1%
1903.02 53
 
0.1%
Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size716.6 KiB
0
73269 
1
18444 

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters91713
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0
2nd row0
3rd row0
4th row1
5th row0

Common Values

ValueCountFrequency (%)
0 73269
79.9%
1 18444
 
20.1%

Length

2023-08-02T15:16:14.317107image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-08-02T15:16:14.494001image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
ValueCountFrequency (%)
0 73269
79.9%
1 18444
 
20.1%

Most occurring characters

ValueCountFrequency (%)
0 73269
79.9%
1 18444
 
20.1%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 91713
100.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 73269
79.9%
1 18444
 
20.1%

Most occurring scripts

ValueCountFrequency (%)
Common 91713
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0 73269
79.9%
1 18444
 
20.1%

Most occurring blocks

ValueCountFrequency (%)
ASCII 91713
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 73269
79.9%
1 18444
 
20.1%

arf_apache
Categorical

Distinct2
Distinct (%)< 0.1%
Missing715
Missing (%)0.8%
Memory size716.6 KiB
0.0
88452 
1.0
 
2546

Length

Max length3
Median length3
Mean length3
Min length3

Characters and Unicode

Total characters272994
Distinct characters3
Distinct categories2 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0.0
2nd row0.0
3rd row0.0
4th row0.0
5th row0.0

Common Values

ValueCountFrequency (%)
0.0 88452
96.4%
1.0 2546
 
2.8%
(Missing) 715
 
0.8%

Length

2023-08-02T15:16:14.653760image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-08-02T15:16:14.845108image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
ValueCountFrequency (%)
0.0 88452
97.2%
1.0 2546
 
2.8%

Most occurring characters

ValueCountFrequency (%)
0 179450
65.7%
. 90998
33.3%
1 2546
 
0.9%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 181996
66.7%
Other Punctuation 90998
33.3%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 179450
98.6%
1 2546
 
1.4%
Other Punctuation
ValueCountFrequency (%)
. 90998
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 272994
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0 179450
65.7%
. 90998
33.3%
1 2546
 
0.9%

Most occurring blocks

ValueCountFrequency (%)
ASCII 272994
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 179450
65.7%
. 90998
33.3%
1 2546
 
0.9%

gcs_eyes_apache
Categorical

HIGH CORRELATION  MISSING 

Distinct4
Distinct (%)< 0.1%
Missing1901
Missing (%)2.1%
Memory size716.6 KiB
4.0
62995 
3.0
13863 
1.0
8274 
2.0
 
4680

Length

Max length3
Median length3
Mean length3
Min length3

Characters and Unicode

Total characters269436
Distinct characters6
Distinct categories2 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row3.0
2nd row1.0
3rd row3.0
4th row4.0
5th row4.0

Common Values

ValueCountFrequency (%)
4.0 62995
68.7%
3.0 13863
 
15.1%
1.0 8274
 
9.0%
2.0 4680
 
5.1%
(Missing) 1901
 
2.1%

Length

2023-08-02T15:16:14.994768image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-08-02T15:16:15.210978image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
ValueCountFrequency (%)
4.0 62995
70.1%
3.0 13863
 
15.4%
1.0 8274
 
9.2%
2.0 4680
 
5.2%

Most occurring characters

ValueCountFrequency (%)
. 89812
33.3%
0 89812
33.3%
4 62995
23.4%
3 13863
 
5.1%
1 8274
 
3.1%
2 4680
 
1.7%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 179624
66.7%
Other Punctuation 89812
33.3%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 89812
50.0%
4 62995
35.1%
3 13863
 
7.7%
1 8274
 
4.6%
2 4680
 
2.6%
Other Punctuation
ValueCountFrequency (%)
. 89812
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 269436
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
. 89812
33.3%
0 89812
33.3%
4 62995
23.4%
3 13863
 
5.1%
1 8274
 
3.1%
2 4680
 
1.7%

Most occurring blocks

ValueCountFrequency (%)
ASCII 269436
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
. 89812
33.3%
0 89812
33.3%
4 62995
23.4%
3 13863
 
5.1%
1 8274
 
3.1%
2 4680
 
1.7%

gcs_motor_apache
Real number (ℝ)

HIGH CORRELATION  MISSING 

Distinct6
Distinct (%)< 0.1%
Missing1901
Missing (%)2.1%
Infinite0
Infinite (%)0.0%
Mean5.4711954
Minimum1
Maximum6
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size716.6 KiB
2023-08-02T15:16:15.366651image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile1
Q16
median6
Q36
95-th percentile6
Maximum6
Range5
Interquartile range (IQR)0

Descriptive statistics

Standard deviation1.2883763
Coefficient of variation (CV)0.2354835
Kurtosis6.3222644
Mean5.4711954
Median Absolute Deviation (MAD)0
Skewness-2.7123756
Sum491379
Variance1.6599134
MonotonicityNot monotonic
2023-08-02T15:16:15.568016image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram with fixed size bins (bins=6)
ValueCountFrequency (%)
6 70960
77.4%
5 7982
 
8.7%
1 5543
 
6.0%
4 4494
 
4.9%
3 524
 
0.6%
2 309
 
0.3%
(Missing) 1901
 
2.1%
ValueCountFrequency (%)
1 5543
 
6.0%
2 309
 
0.3%
3 524
 
0.6%
4 4494
 
4.9%
5 7982
 
8.7%
6 70960
77.4%
ValueCountFrequency (%)
6 70960
77.4%
5 7982
 
8.7%
4 4494
 
4.9%
3 524
 
0.6%
2 309
 
0.3%
1 5543
 
6.0%

gcs_unable_apache
Categorical

HIGH CORRELATION  IMBALANCE  MISSING 

Distinct2
Distinct (%)< 0.1%
Missing1037
Missing (%)1.1%
Memory size716.6 KiB
0.0
89812 
1.0
 
864

Length

Max length3
Median length3
Mean length3
Min length3

Characters and Unicode

Total characters272028
Distinct characters3
Distinct categories2 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0.0
2nd row0.0
3rd row0.0
4th row0.0
5th row0.0

Common Values

ValueCountFrequency (%)
0.0 89812
97.9%
1.0 864
 
0.9%
(Missing) 1037
 
1.1%

Length

2023-08-02T15:16:15.737796image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-08-02T15:16:15.898494image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
ValueCountFrequency (%)
0.0 89812
99.0%
1.0 864
 
1.0%

Most occurring characters

ValueCountFrequency (%)
0 180488
66.3%
. 90676
33.3%
1 864
 
0.3%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 181352
66.7%
Other Punctuation 90676
33.3%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 180488
99.5%
1 864
 
0.5%
Other Punctuation
ValueCountFrequency (%)
. 90676
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 272028
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0 180488
66.3%
. 90676
33.3%
1 864
 
0.3%

Most occurring blocks

ValueCountFrequency (%)
ASCII 272028
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 180488
66.3%
. 90676
33.3%
1 864
 
0.3%

gcs_verbal_apache
Categorical

HIGH CORRELATION  MISSING 

Distinct5
Distinct (%)< 0.1%
Missing1901
Missing (%)2.1%
Memory size716.6 KiB
5.0
56909 
1.0
16741 
4.0
10947 
3.0
 
3275
2.0
 
1940

Length

Max length3
Median length3
Mean length3
Min length3

Characters and Unicode

Total characters269436
Distinct characters7
Distinct categories2 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row4.0
2nd row1.0
3rd row5.0
4th row5.0
5th row5.0

Common Values

ValueCountFrequency (%)
5.0 56909
62.1%
1.0 16741
 
18.3%
4.0 10947
 
11.9%
3.0 3275
 
3.6%
2.0 1940
 
2.1%
(Missing) 1901
 
2.1%

Length

2023-08-02T15:16:16.023264image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-08-02T15:16:16.205682image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
ValueCountFrequency (%)
5.0 56909
63.4%
1.0 16741
 
18.6%
4.0 10947
 
12.2%
3.0 3275
 
3.6%
2.0 1940
 
2.2%

Most occurring characters

ValueCountFrequency (%)
. 89812
33.3%
0 89812
33.3%
5 56909
21.1%
1 16741
 
6.2%
4 10947
 
4.1%
3 3275
 
1.2%
2 1940
 
0.7%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 179624
66.7%
Other Punctuation 89812
33.3%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 89812
50.0%
5 56909
31.7%
1 16741
 
9.3%
4 10947
 
6.1%
3 3275
 
1.8%
2 1940
 
1.1%
Other Punctuation
ValueCountFrequency (%)
. 89812
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 269436
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
. 89812
33.3%
0 89812
33.3%
5 56909
21.1%
1 16741
 
6.2%
4 10947
 
4.1%
3 3275
 
1.2%
2 1940
 
0.7%

Most occurring blocks

ValueCountFrequency (%)
ASCII 269436
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
. 89812
33.3%
0 89812
33.3%
5 56909
21.1%
1 16741
 
6.2%
4 10947
 
4.1%
3 3275
 
1.2%
2 1940
 
0.7%

heart_rate_apache
Real number (ℝ)

Distinct149
Distinct (%)0.2%
Missing878
Missing (%)1.0%
Infinite0
Infinite (%)0.0%
Mean99.707932
Minimum30
Maximum178
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size716.6 KiB
2023-08-02T15:16:16.399318image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/

Quantile statistics

Minimum30
5-th percentile47
Q186
median104
Q3120
95-th percentile146
Maximum178
Range148
Interquartile range (IQR)34

Descriptive statistics

Standard deviation30.870502
Coefficient of variation (CV)0.30960929
Kurtosis-0.44843933
Mean99.707932
Median Absolute Deviation (MAD)16
Skewness-0.26730015
Sum9056970
Variance952.98791
MonotonicityNot monotonic
2023-08-02T15:16:16.603374image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
100 1860
 
2.0%
108 1806
 
2.0%
102 1794
 
2.0%
104 1724
 
1.9%
98 1716
 
1.9%
106 1689
 
1.8%
110 1638
 
1.8%
96 1634
 
1.8%
60 1624
 
1.8%
112 1609
 
1.8%
Other values (139) 73741
80.4%
ValueCountFrequency (%)
30 561
0.6%
31 73
 
0.1%
32 105
 
0.1%
33 79
 
0.1%
34 111
 
0.1%
35 108
 
0.1%
36 127
 
0.1%
37 130
 
0.1%
38 171
 
0.2%
39 175
 
0.2%
ValueCountFrequency (%)
178 462
0.5%
177 33
 
< 0.1%
176 37
 
< 0.1%
175 46
 
0.1%
174 57
 
0.1%
173 38
 
< 0.1%
172 56
 
0.1%
171 35
 
< 0.1%
170 70
 
0.1%
169 64
 
0.1%

intubated_apache
Categorical

Distinct2
Distinct (%)< 0.1%
Missing715
Missing (%)0.8%
Memory size716.6 KiB
0.0
77237 
1.0
13761 

Length

Max length3
Median length3
Mean length3
Min length3

Characters and Unicode

Total characters272994
Distinct characters3
Distinct categories2 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0.0
2nd row0.0
3rd row0.0
4th row1.0
5th row0.0

Common Values

ValueCountFrequency (%)
0.0 77237
84.2%
1.0 13761
 
15.0%
(Missing) 715
 
0.8%

Length

2023-08-02T15:16:16.793256image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-08-02T15:16:16.949697image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
ValueCountFrequency (%)
0.0 77237
84.9%
1.0 13761
 
15.1%

Most occurring characters

ValueCountFrequency (%)
0 168235
61.6%
. 90998
33.3%
1 13761
 
5.0%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 181996
66.7%
Other Punctuation 90998
33.3%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 168235
92.4%
1 13761
 
7.6%
Other Punctuation
ValueCountFrequency (%)
. 90998
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 272994
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0 168235
61.6%
. 90998
33.3%
1 13761
 
5.0%

Most occurring blocks

ValueCountFrequency (%)
ASCII 272994
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 168235
61.6%
. 90998
33.3%
1 13761
 
5.0%

map_apache
Real number (ℝ)

Distinct161
Distinct (%)0.2%
Missing994
Missing (%)1.1%
Infinite0
Infinite (%)0.0%
Mean88.015873
Minimum40
Maximum200
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size716.6 KiB
2023-08-02T15:16:17.109071image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/

Quantile statistics

Minimum40
5-th percentile42
Q154
median67
Q3125
95-th percentile164
Maximum200
Range160
Interquartile range (IQR)71

Descriptive statistics

Standard deviation42.032412
Coefficient of variation (CV)0.4775549
Kurtosis-0.79373791
Mean88.015873
Median Absolute Deviation (MAD)21
Skewness0.69831947
Sum7984712
Variance1766.7236
MonotonicityNot monotonic
2023-08-02T15:16:17.806141image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
56 2122
 
2.3%
54 2029
 
2.2%
60 1971
 
2.1%
58 1955
 
2.1%
53 1893
 
2.1%
55 1882
 
2.1%
52 1877
 
2.0%
40 1874
 
2.0%
57 1844
 
2.0%
51 1814
 
2.0%
Other values (151) 71458
77.9%
ValueCountFrequency (%)
40 1874
2.0%
41 1450
1.6%
42 1383
1.5%
43 1358
1.5%
44 1360
1.5%
45 1333
1.5%
46 1435
1.6%
47 1507
1.6%
48 1654
1.8%
49 1579
1.7%
ValueCountFrequency (%)
200 141
0.2%
199 128
0.1%
198 100
0.1%
197 113
0.1%
196 121
0.1%
195 121
0.1%
194 109
0.1%
193 90
0.1%
192 109
0.1%
191 95
0.1%

resprate_apache
Real number (ℝ)

Distinct74
Distinct (%)0.1%
Missing1234
Missing (%)1.3%
Infinite0
Infinite (%)0.0%
Mean25.811007
Minimum4
Maximum60
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size716.6 KiB
2023-08-02T15:16:17.988432image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/

Quantile statistics

Minimum4
5-th percentile5
Q111
median28
Q336
95-th percentile53
Maximum60
Range56
Interquartile range (IQR)25

Descriptive statistics

Standard deviation15.106312
Coefficient of variation (CV)0.5852663
Kurtosis-0.92766851
Mean25.811007
Median Absolute Deviation (MAD)14
Skewness0.25849416
Sum2335354.1
Variance228.20068
MonotonicityNot monotonic
2023-08-02T15:16:18.166812image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
10 4303
 
4.7%
12 4221
 
4.6%
11 3911
 
4.3%
4 3528
 
3.8%
9 3432
 
3.7%
30 3145
 
3.4%
28 3074
 
3.4%
8 2927
 
3.2%
29 2887
 
3.1%
31 2725
 
3.0%
Other values (64) 56326
61.4%
ValueCountFrequency (%)
4 3528
3.8%
5 2076
2.3%
5.9 1
 
< 0.1%
6 2091
2.3%
7 2415
2.6%
7.1 2
 
< 0.1%
7.2 1
 
< 0.1%
7.8 1
 
< 0.1%
8 2927
3.2%
8.4 1
 
< 0.1%
ValueCountFrequency (%)
60 937
1.0%
59 666
0.7%
58 516
0.6%
57 509
0.6%
56 494
0.5%
55 511
0.6%
54 512
0.6%
53 524
0.6%
52 569
0.6%
51 554
0.6%

temp_apache
Real number (ℝ)

Distinct191
Distinct (%)0.2%
Missing4108
Missing (%)4.5%
Infinite0
Infinite (%)0.0%
Mean36.414472
Minimum32.1
Maximum39.7
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size716.6 KiB
2023-08-02T15:16:18.369694image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/

Quantile statistics

Minimum32.1
5-th percentile35.3
Q136.2
median36.5
Q336.7
95-th percentile37.3
Maximum39.7
Range7.6
Interquartile range (IQR)0.5

Descriptive statistics

Standard deviation0.83349642
Coefficient of variation (CV)0.022889153
Kurtosis8.9838609
Mean36.414472
Median Absolute Deviation (MAD)0.3
Skewness-0.96629929
Sum3190089.9
Variance0.69471627
MonotonicityNot monotonic
2023-08-02T15:16:18.582375image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
36.4 9347
 
10.2%
36.6 8572
 
9.3%
36.7 8076
 
8.8%
36.3 6667
 
7.3%
36.5 6193
 
6.8%
36.8 5778
 
6.3%
36.2 4802
 
5.2%
36.1 4594
 
5.0%
36.9 3698
 
4.0%
36 2807
 
3.1%
Other values (181) 27071
29.5%
(Missing) 4108
 
4.5%
ValueCountFrequency (%)
32.1 516
0.6%
32.16 1
 
< 0.1%
32.2 62
 
0.1%
32.22 1
 
< 0.1%
32.27 1
 
< 0.1%
32.3 53
 
0.1%
32.33 1
 
< 0.1%
32.4 49
 
0.1%
32.5 56
 
0.1%
32.6 70
 
0.1%
ValueCountFrequency (%)
39.7 370
0.4%
39.66 4
 
< 0.1%
39.61 5
 
< 0.1%
39.6 95
 
0.1%
39.55 9
 
< 0.1%
39.5 132
 
0.1%
39.44 9
 
< 0.1%
39.4 211
0.2%
39.39 1
 
< 0.1%
39.38 9
 
< 0.1%
Distinct2
Distinct (%)< 0.1%
Missing715
Missing (%)0.8%
Memory size716.6 KiB
0.0
61358 
1.0
29640 

Length

Max length3
Median length3
Mean length3
Min length3

Characters and Unicode

Total characters272994
Distinct characters3
Distinct categories2 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0.0
2nd row1.0
3rd row0.0
4th row1.0
5th row0.0

Common Values

ValueCountFrequency (%)
0.0 61358
66.9%
1.0 29640
32.3%
(Missing) 715
 
0.8%

Length

2023-08-02T15:16:18.783113image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-08-02T15:16:18.940634image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
ValueCountFrequency (%)
0.0 61358
67.4%
1.0 29640
32.6%

Most occurring characters

ValueCountFrequency (%)
0 152356
55.8%
. 90998
33.3%
1 29640
 
10.9%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 181996
66.7%
Other Punctuation 90998
33.3%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 152356
83.7%
1 29640
 
16.3%
Other Punctuation
ValueCountFrequency (%)
. 90998
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 272994
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0 152356
55.8%
. 90998
33.3%
1 29640
 
10.9%

Most occurring blocks

ValueCountFrequency (%)
ASCII 272994
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 152356
55.8%
. 90998
33.3%
1 29640
 
10.9%

apache_4a_hospital_death_prob
Real number (ℝ)

HIGH CORRELATION  MISSING  ZEROS 

Distinct101
Distinct (%)0.1%
Missing7947
Missing (%)8.7%
Infinite0
Infinite (%)0.0%
Mean0.086786883
Minimum-1
Maximum0.99
Zeros2488
Zeros (%)2.7%
Negative2371
Negative (%)2.6%
Memory size716.6 KiB
2023-08-02T15:16:19.104597image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/

Quantile statistics

Minimum-1
5-th percentile0
Q10.02
median0.05
Q30.13
95-th percentile0.5
Maximum0.99
Range1.99
Interquartile range (IQR)0.11

Descriptive statistics

Standard deviation0.24756854
Coefficient of variation (CV)2.8526032
Kurtosis9.8256703
Mean0.086786883
Median Absolute Deviation (MAD)0.04
Skewness-1.4840473
Sum7269.79
Variance0.06129018
MonotonicityNot monotonic
2023-08-02T15:16:19.314108image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0.01 11023
 
12.0%
0.02 9970
 
10.9%
0.03 7504
 
8.2%
0.04 5996
 
6.5%
0.05 4869
 
5.3%
0.06 3849
 
4.2%
0.07 3259
 
3.6%
0.08 2787
 
3.0%
0 2488
 
2.7%
0.09 2396
 
2.6%
Other values (91) 29625
32.3%
(Missing) 7947
 
8.7%
ValueCountFrequency (%)
-1 2371
 
2.6%
0 2488
 
2.7%
0.01 11023
12.0%
0.02 9970
10.9%
0.03 7504
8.2%
0.04 5996
6.5%
0.05 4869
5.3%
0.06 3849
 
4.2%
0.07 3259
 
3.6%
0.08 2787
 
3.0%
ValueCountFrequency (%)
0.99 1
 
< 0.1%
0.98 5
 
< 0.1%
0.97 11
 
< 0.1%
0.96 21
< 0.1%
0.95 22
< 0.1%
0.94 38
< 0.1%
0.93 36
< 0.1%
0.92 49
0.1%
0.91 44
< 0.1%
0.9 49
0.1%

apache_4a_icu_death_prob
Real number (ℝ)

HIGH CORRELATION  MISSING  ZEROS 

Distinct99
Distinct (%)0.1%
Missing7947
Missing (%)8.7%
Infinite0
Infinite (%)0.0%
Mean0.043954827
Minimum-1
Maximum0.97
Zeros9694
Zeros (%)10.6%
Negative2230
Negative (%)2.4%
Memory size716.6 KiB
2023-08-02T15:16:19.534774image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/

Quantile statistics

Minimum-1
5-th percentile0
Q10.01
median0.02
Q30.06
95-th percentile0.35
Maximum0.97
Range1.97
Interquartile range (IQR)0.05

Descriptive statistics

Standard deviation0.21734138
Coefficient of variation (CV)4.9446533
Kurtosis13.830907
Mean0.043954827
Median Absolute Deviation (MAD)0.02
Skewness-2.0288356
Sum3681.92
Variance0.047237274
MonotonicityNot monotonic
2023-08-02T15:16:19.779816image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0.01 21087
23.0%
0.02 11733
12.8%
0 9694
10.6%
0.03 7253
 
7.9%
0.04 5068
 
5.5%
0.05 3680
 
4.0%
0.06 2758
 
3.0%
-1 2230
 
2.4%
0.07 2154
 
2.3%
0.08 1760
 
1.9%
Other values (89) 16349
17.8%
(Missing) 7947
 
8.7%
ValueCountFrequency (%)
-1 2230
 
2.4%
0 9694
10.6%
0.01 21087
23.0%
0.02 11733
12.8%
0.03 7253
 
7.9%
0.04 5068
 
5.5%
0.05 3680
 
4.0%
0.06 2758
 
3.0%
0.07 2154
 
2.3%
0.08 1760
 
1.9%
ValueCountFrequency (%)
0.97 4
 
< 0.1%
0.96 3
 
< 0.1%
0.95 6
 
< 0.1%
0.94 11
< 0.1%
0.93 16
< 0.1%
0.92 12
< 0.1%
0.91 19
< 0.1%
0.9 25
< 0.1%
0.89 23
< 0.1%
0.88 21
< 0.1%

aids
Categorical

Distinct2
Distinct (%)< 0.1%
Missing715
Missing (%)0.8%
Memory size716.6 KiB
0.0
90920 
1.0
 
78

Length

Max length3
Median length3
Mean length3
Min length3

Characters and Unicode

Total characters272994
Distinct characters3
Distinct categories2 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0.0
2nd row0.0
3rd row0.0
4th row0.0
5th row0.0

Common Values

ValueCountFrequency (%)
0.0 90920
99.1%
1.0 78
 
0.1%
(Missing) 715
 
0.8%

Length

2023-08-02T15:16:20.028425image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-08-02T15:16:20.199620image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
ValueCountFrequency (%)
0.0 90920
99.9%
1.0 78
 
0.1%

Most occurring characters

ValueCountFrequency (%)
0 181918
66.6%
. 90998
33.3%
1 78
 
< 0.1%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 181996
66.7%
Other Punctuation 90998
33.3%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 181918
> 99.9%
1 78
 
< 0.1%
Other Punctuation
ValueCountFrequency (%)
. 90998
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 272994
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0 181918
66.6%
. 90998
33.3%
1 78
 
< 0.1%

Most occurring blocks

ValueCountFrequency (%)
ASCII 272994
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 181918
66.6%
. 90998
33.3%
1 78
 
< 0.1%

cirrhosis
Categorical

HIGH CORRELATION  IMBALANCE 

Distinct2
Distinct (%)< 0.1%
Missing715
Missing (%)0.8%
Memory size716.6 KiB
0.0
89570 
1.0
 
1428

Length

Max length3
Median length3
Mean length3
Min length3

Characters and Unicode

Total characters272994
Distinct characters3
Distinct categories2 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0.0
2nd row0.0
3rd row0.0
4th row0.0
5th row0.0

Common Values

ValueCountFrequency (%)
0.0 89570
97.7%
1.0 1428
 
1.6%
(Missing) 715
 
0.8%

Length

2023-08-02T15:16:20.345304image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-08-02T15:16:20.534118image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
ValueCountFrequency (%)
0.0 89570
98.4%
1.0 1428
 
1.6%

Most occurring characters

ValueCountFrequency (%)
0 180568
66.1%
. 90998
33.3%
1 1428
 
0.5%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 181996
66.7%
Other Punctuation 90998
33.3%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 180568
99.2%
1 1428
 
0.8%
Other Punctuation
ValueCountFrequency (%)
. 90998
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 272994
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0 180568
66.1%
. 90998
33.3%
1 1428
 
0.5%

Most occurring blocks

ValueCountFrequency (%)
ASCII 272994
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 180568
66.1%
. 90998
33.3%
1 1428
 
0.5%
Distinct2
Distinct (%)< 0.1%
Missing715
Missing (%)0.8%
Memory size716.6 KiB
0.0
70506 
1.0
20492 

Length

Max length3
Median length3
Mean length3
Min length3

Characters and Unicode

Total characters272994
Distinct characters3
Distinct categories2 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row1.0
2nd row1.0
3rd row0.0
4th row0.0
5th row0.0

Common Values

ValueCountFrequency (%)
0.0 70506
76.9%
1.0 20492
 
22.3%
(Missing) 715
 
0.8%

Length

2023-08-02T15:16:20.669850image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-08-02T15:16:20.824512image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
ValueCountFrequency (%)
0.0 70506
77.5%
1.0 20492
 
22.5%

Most occurring characters

ValueCountFrequency (%)
0 161504
59.2%
. 90998
33.3%
1 20492
 
7.5%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 181996
66.7%
Other Punctuation 90998
33.3%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 161504
88.7%
1 20492
 
11.3%
Other Punctuation
ValueCountFrequency (%)
. 90998
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 272994
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0 161504
59.2%
. 90998
33.3%
1 20492
 
7.5%

Most occurring blocks

ValueCountFrequency (%)
ASCII 272994
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 161504
59.2%
. 90998
33.3%
1 20492
 
7.5%

hepatic_failure
Categorical

HIGH CORRELATION  IMBALANCE 

Distinct2
Distinct (%)< 0.1%
Missing715
Missing (%)0.8%
Memory size716.6 KiB
0.0
89816 
1.0
 
1182

Length

Max length3
Median length3
Mean length3
Min length3

Characters and Unicode

Total characters272994
Distinct characters3
Distinct categories2 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0.0
2nd row0.0
3rd row0.0
4th row0.0
5th row0.0

Common Values

ValueCountFrequency (%)
0.0 89816
97.9%
1.0 1182
 
1.3%
(Missing) 715
 
0.8%

Length

2023-08-02T15:16:20.954145image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-08-02T15:16:21.107283image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
ValueCountFrequency (%)
0.0 89816
98.7%
1.0 1182
 
1.3%

Most occurring characters

ValueCountFrequency (%)
0 180814
66.2%
. 90998
33.3%
1 1182
 
0.4%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 181996
66.7%
Other Punctuation 90998
33.3%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 180814
99.4%
1 1182
 
0.6%
Other Punctuation
ValueCountFrequency (%)
. 90998
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 272994
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0 180814
66.2%
. 90998
33.3%
1 1182
 
0.4%

Most occurring blocks

ValueCountFrequency (%)
ASCII 272994
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 180814
66.2%
. 90998
33.3%
1 1182
 
0.4%
Distinct2
Distinct (%)< 0.1%
Missing715
Missing (%)0.8%
Memory size716.6 KiB
0.0
88617 
1.0
 
2381

Length

Max length3
Median length3
Mean length3
Min length3

Characters and Unicode

Total characters272994
Distinct characters3
Distinct categories2 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0.0
2nd row0.0
3rd row0.0
4th row0.0
5th row0.0

Common Values

ValueCountFrequency (%)
0.0 88617
96.6%
1.0 2381
 
2.6%
(Missing) 715
 
0.8%

Length

2023-08-02T15:16:21.232204image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-08-02T15:16:21.384168image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
ValueCountFrequency (%)
0.0 88617
97.4%
1.0 2381
 
2.6%

Most occurring characters

ValueCountFrequency (%)
0 179615
65.8%
. 90998
33.3%
1 2381
 
0.9%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 181996
66.7%
Other Punctuation 90998
33.3%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 179615
98.7%
1 2381
 
1.3%
Other Punctuation
ValueCountFrequency (%)
. 90998
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 272994
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0 179615
65.8%
. 90998
33.3%
1 2381
 
0.9%

Most occurring blocks

ValueCountFrequency (%)
ASCII 272994
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 179615
65.8%
. 90998
33.3%
1 2381
 
0.9%

leukemia
Categorical

Distinct2
Distinct (%)< 0.1%
Missing715
Missing (%)0.8%
Memory size716.6 KiB
0.0
90355 
1.0
 
643

Length

Max length3
Median length3
Mean length3
Min length3

Characters and Unicode

Total characters272994
Distinct characters3
Distinct categories2 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0.0
2nd row0.0
3rd row0.0
4th row0.0
5th row0.0

Common Values

ValueCountFrequency (%)
0.0 90355
98.5%
1.0 643
 
0.7%
(Missing) 715
 
0.8%

Length

2023-08-02T15:16:21.512726image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-08-02T15:16:21.663198image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
ValueCountFrequency (%)
0.0 90355
99.3%
1.0 643
 
0.7%

Most occurring characters

ValueCountFrequency (%)
0 181353
66.4%
. 90998
33.3%
1 643
 
0.2%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 181996
66.7%
Other Punctuation 90998
33.3%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 181353
99.6%
1 643
 
0.4%
Other Punctuation
ValueCountFrequency (%)
. 90998
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 272994
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0 181353
66.4%
. 90998
33.3%
1 643
 
0.2%

Most occurring blocks

ValueCountFrequency (%)
ASCII 272994
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 181353
66.4%
. 90998
33.3%
1 643
 
0.2%

lymphoma
Categorical

Distinct2
Distinct (%)< 0.1%
Missing715
Missing (%)0.8%
Memory size716.6 KiB
0.0
90622 
1.0
 
376

Length

Max length3
Median length3
Mean length3
Min length3

Characters and Unicode

Total characters272994
Distinct characters3
Distinct categories2 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0.0
2nd row0.0
3rd row0.0
4th row0.0
5th row0.0

Common Values

ValueCountFrequency (%)
0.0 90622
98.8%
1.0 376
 
0.4%
(Missing) 715
 
0.8%

Length

2023-08-02T15:16:21.785244image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-08-02T15:16:21.936365image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
ValueCountFrequency (%)
0.0 90622
99.6%
1.0 376
 
0.4%

Most occurring characters

ValueCountFrequency (%)
0 181620
66.5%
. 90998
33.3%
1 376
 
0.1%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 181996
66.7%
Other Punctuation 90998
33.3%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 181620
99.8%
1 376
 
0.2%
Other Punctuation
ValueCountFrequency (%)
. 90998
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 272994
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0 181620
66.5%
. 90998
33.3%
1 376
 
0.1%

Most occurring blocks

ValueCountFrequency (%)
ASCII 272994
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 181620
66.5%
. 90998
33.3%
1 376
 
0.1%
Distinct2
Distinct (%)< 0.1%
Missing715
Missing (%)0.8%
Memory size716.6 KiB
0.0
89120 
1.0
 
1878

Length

Max length3
Median length3
Mean length3
Min length3

Characters and Unicode

Total characters272994
Distinct characters3
Distinct categories2 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0.0
2nd row0.0
3rd row0.0
4th row0.0
5th row0.0

Common Values

ValueCountFrequency (%)
0.0 89120
97.2%
1.0 1878
 
2.0%
(Missing) 715
 
0.8%

Length

2023-08-02T15:16:22.060613image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-08-02T15:16:22.207801image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
ValueCountFrequency (%)
0.0 89120
97.9%
1.0 1878
 
2.1%

Most occurring characters

ValueCountFrequency (%)
0 180118
66.0%
. 90998
33.3%
1 1878
 
0.7%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 181996
66.7%
Other Punctuation 90998
33.3%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 180118
99.0%
1 1878
 
1.0%
Other Punctuation
ValueCountFrequency (%)
. 90998
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 272994
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0 180118
66.0%
. 90998
33.3%
1 1878
 
0.7%

Most occurring blocks

ValueCountFrequency (%)
ASCII 272994
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 180118
66.0%
. 90998
33.3%
1 1878
 
0.7%

apache_3j_bodysystem
Categorical

HIGH CORRELATION  MISSING 

Distinct11
Distinct (%)< 0.1%
Missing1662
Missing (%)1.8%
Memory size716.6 KiB
Cardiovascular
29999 
Neurological
11896 
Sepsis
11740 
Respiratory
11609 
Gastrointestinal
9026 
Other values (6)
15781 

Length

Max length20
Median length16
Mean length11.783478
Min length6

Characters and Unicode

Total characters1061114
Distinct characters28
Distinct categories3 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowSepsis
2nd rowRespiratory
3rd rowMetabolic
4th rowCardiovascular
5th rowTrauma

Common Values

ValueCountFrequency (%)
Cardiovascular 29999
32.7%
Neurological 11896
 
13.0%
Sepsis 11740
 
12.8%
Respiratory 11609
 
12.7%
Gastrointestinal 9026
 
9.8%
Metabolic 7650
 
8.3%
Trauma 3842
 
4.2%
Genitourinary 2172
 
2.4%
Musculoskeletal/Skin 1166
 
1.3%
Hematological 638
 
0.7%
(Missing) 1662
 
1.8%

Length

2023-08-02T15:16:22.353664image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
cardiovascular 29999
33.3%
neurological 11896
 
13.2%
sepsis 11740
 
13.0%
respiratory 11609
 
12.9%
gastrointestinal 9026
 
10.0%
metabolic 7650
 
8.5%
trauma 3842
 
4.3%
genitourinary 2172
 
2.4%
musculoskeletal/skin 1166
 
1.3%
hematological 638
 
0.7%

Most occurring characters

ValueCountFrequency (%)
a 151815
14.3%
r 112324
10.6%
i 97407
9.2%
o 87316
 
8.2%
s 85472
 
8.1%
l 75867
 
7.1%
e 57376
 
5.4%
c 51975
 
4.9%
t 50313
 
4.7%
u 50241
 
4.7%
Other values (18) 241008
22.7%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter 968731
91.3%
Uppercase Letter 91217
 
8.6%
Other Punctuation 1166
 
0.1%

Most frequent character per category

Lowercase Letter
ValueCountFrequency (%)
a 151815
15.7%
r 112324
11.6%
i 97407
10.1%
o 87316
9.0%
s 85472
8.8%
l 75867
7.8%
e 57376
 
5.9%
c 51975
 
5.4%
t 50313
 
5.2%
u 50241
 
5.2%
Other values (9) 148625
15.3%
Uppercase Letter
ValueCountFrequency (%)
C 29999
32.9%
S 12906
14.1%
N 11896
 
13.0%
R 11609
 
12.7%
G 11511
 
12.6%
M 8816
 
9.7%
T 3842
 
4.2%
H 638
 
0.7%
Other Punctuation
ValueCountFrequency (%)
/ 1166
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin 1059948
99.9%
Common 1166
 
0.1%

Most frequent character per script

Latin
ValueCountFrequency (%)
a 151815
14.3%
r 112324
10.6%
i 97407
9.2%
o 87316
 
8.2%
s 85472
 
8.1%
l 75867
 
7.2%
e 57376
 
5.4%
c 51975
 
4.9%
t 50313
 
4.7%
u 50241
 
4.7%
Other values (17) 239842
22.6%
Common
ValueCountFrequency (%)
/ 1166
100.0%

Most occurring blocks

ValueCountFrequency (%)
ASCII 1061114
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
a 151815
14.3%
r 112324
10.6%
i 97407
9.2%
o 87316
 
8.2%
s 85472
 
8.1%
l 75867
 
7.1%
e 57376
 
5.4%
c 51975
 
4.9%
t 50313
 
4.7%
u 50241
 
4.7%
Other values (18) 241008
22.7%

apache_2_bodysystem
Categorical

HIGH CORRELATION  MISSING 

Distinct10
Distinct (%)< 0.1%
Missing1662
Missing (%)1.8%
Memory size716.6 KiB
Cardiovascular
38816 
Neurologic
11896 
Respiratory
11609 
Gastrointestinal
9026 
Metabolic
7650 
Other values (5)
11054 

Length

Max length19
Median length16
Mean length12.870074
Min length6

Characters and Unicode

Total characters1158963
Distinct characters30
Distinct categories4 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowCardiovascular
2nd rowRespiratory
3rd rowMetabolic
4th rowCardiovascular
5th rowTrauma

Common Values

ValueCountFrequency (%)
Cardiovascular 38816
42.3%
Neurologic 11896
 
13.0%
Respiratory 11609
 
12.7%
Gastrointestinal 9026
 
9.8%
Metabolic 7650
 
8.3%
Trauma 3842
 
4.2%
Undefined diagnoses 3768
 
4.1%
Renal/Genitourinary 2460
 
2.7%
Haematologic 638
 
0.7%
Undefined Diagnoses 346
 
0.4%
(Missing) 1662
 
1.8%

Length

2023-08-02T15:16:22.539962image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-08-02T15:16:22.743562image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
ValueCountFrequency (%)
cardiovascular 38816
41.2%
neurologic 11896
 
12.6%
respiratory 11609
 
12.3%
gastrointestinal 9026
 
9.6%
metabolic 7650
 
8.1%
undefined 4114
 
4.4%
diagnoses 4114
 
4.4%
trauma 3842
 
4.1%
renal/genitourinary 2460
 
2.6%
haematologic 638
 
0.7%

Most occurring characters

ValueCountFrequency (%)
a 171753
14.8%
r 130534
11.3%
i 101809
 
8.8%
o 98743
 
8.5%
s 76705
 
6.6%
l 70486
 
6.1%
c 59000
 
5.1%
e 58081
 
5.0%
u 57014
 
4.9%
d 50812
 
4.4%
Other values (20) 284026
24.5%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter 1059532
91.4%
Uppercase Letter 92857
 
8.0%
Space Separator 4114
 
0.4%
Other Punctuation 2460
 
0.2%

Most frequent character per category

Lowercase Letter
ValueCountFrequency (%)
a 171753
16.2%
r 130534
12.3%
i 101809
9.6%
o 98743
9.3%
s 76705
7.2%
l 70486
 
6.7%
c 59000
 
5.6%
e 58081
 
5.5%
u 57014
 
5.4%
d 50812
 
4.8%
Other values (9) 184595
17.4%
Uppercase Letter
ValueCountFrequency (%)
C 38816
41.8%
R 14069
 
15.2%
N 11896
 
12.8%
G 11486
 
12.4%
M 7650
 
8.2%
U 4114
 
4.4%
T 3842
 
4.1%
H 638
 
0.7%
D 346
 
0.4%
Space Separator
ValueCountFrequency (%)
4114
100.0%
Other Punctuation
ValueCountFrequency (%)
/ 2460
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin 1152389
99.4%
Common 6574
 
0.6%

Most frequent character per script

Latin
ValueCountFrequency (%)
a 171753
14.9%
r 130534
11.3%
i 101809
 
8.8%
o 98743
 
8.6%
s 76705
 
6.7%
l 70486
 
6.1%
c 59000
 
5.1%
e 58081
 
5.0%
u 57014
 
4.9%
d 50812
 
4.4%
Other values (18) 277452
24.1%
Common
ValueCountFrequency (%)
4114
62.6%
/ 2460
37.4%

Most occurring blocks

ValueCountFrequency (%)
ASCII 1158963
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
a 171753
14.8%
r 130534
11.3%
i 101809
 
8.8%
o 98743
 
8.5%
s 76705
 
6.6%
l 70486
 
6.1%
c 59000
 
5.1%
e 58081
 
5.0%
u 57014
 
4.9%
d 50812
 
4.4%
Other values (20) 284026
24.5%

Unnamed: 83
Unsupported

MISSING  REJECTED  UNSUPPORTED 

Missing91713
Missing (%)100.0%
Memory size716.6 KiB

hospital_death
Categorical

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size716.6 KiB
0
83798 
1
 
7915

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters91713
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0
2nd row0
3rd row0
4th row0
5th row0

Common Values

ValueCountFrequency (%)
0 83798
91.4%
1 7915
 
8.6%

Length

2023-08-02T15:16:22.934624image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-08-02T15:16:23.080551image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
ValueCountFrequency (%)
0 83798
91.4%
1 7915
 
8.6%

Most occurring characters

ValueCountFrequency (%)
0 83798
91.4%
1 7915
 
8.6%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 91713
100.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 83798
91.4%
1 7915
 
8.6%

Most occurring scripts

ValueCountFrequency (%)
Common 91713
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0 83798
91.4%
1 7915
 
8.6%

Most occurring blocks

ValueCountFrequency (%)
ASCII 91713
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 83798
91.4%
1 7915
 
8.6%

Interactions

2023-08-02T15:15:58.391968image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-08-02T15:15:00.640193image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-08-02T15:15:03.389534image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-08-02T15:15:06.214774image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-08-02T15:15:08.979933image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-08-02T15:15:12.022004image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-08-02T15:15:14.980892image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-08-02T15:15:18.360469image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-08-02T15:15:21.534030image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-08-02T15:15:25.499270image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-08-02T15:15:29.564095image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-08-02T15:15:32.914389image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-08-02T15:15:36.064971image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-08-02T15:15:39.215358image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-08-02T15:15:42.972521image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-08-02T15:15:46.703439image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-08-02T15:15:50.401624image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-08-02T15:15:54.093594image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-08-02T15:15:58.588114image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-08-02T15:15:00.798534image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-08-02T15:15:03.545442image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-08-02T15:15:06.416375image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-08-02T15:15:09.126120image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-08-02T15:15:12.173436image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-08-02T15:15:15.119834image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-08-02T15:15:18.565565image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-08-02T15:15:21.735864image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-08-02T15:15:25.694295image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-08-02T15:15:29.725558image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-08-02T15:15:33.078831image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-08-02T15:15:36.236118image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-08-02T15:15:39.393177image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-08-02T15:15:43.192648image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-08-02T15:15:46.888828image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-08-02T15:15:50.600648image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-08-02T15:15:54.387183image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-08-02T15:15:59.242594image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-08-02T15:15:00.927300image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-08-02T15:15:03.687305image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-08-02T15:15:06.551792image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-08-02T15:15:09.275202image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-08-02T15:15:12.320347image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-08-02T15:15:15.274671image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-08-02T15:15:18.763118image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-08-02T15:15:21.936824image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-08-02T15:15:25.922877image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-08-02T15:15:29.887945image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-08-02T15:15:33.242428image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-08-02T15:15:36.408128image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-08-02T15:15:39.581367image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-08-02T15:15:43.406368image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-08-02T15:15:47.068269image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-08-02T15:15:50.822526image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-08-02T15:15:54.681634image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-08-02T15:15:59.430829image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-08-02T15:15:01.068892image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-08-02T15:15:03.841032image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-08-02T15:15:06.701480image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-08-02T15:15:09.437114image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-08-02T15:15:12.479960image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-08-02T15:15:15.502594image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-08-02T15:15:18.910029image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-08-02T15:15:22.154210image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-08-02T15:15:26.230947image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-08-02T15:15:30.068434image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-08-02T15:15:33.425745image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-08-02T15:15:36.594376image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-08-02T15:15:39.776236image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-08-02T15:15:43.637907image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-08-02T15:15:47.263356image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-08-02T15:15:51.020129image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-08-02T15:15:54.969417image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-08-02T15:15:59.615828image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-08-02T15:15:01.211347image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-08-02T15:15:03.991527image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-08-02T15:15:06.852440image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-08-02T15:15:09.633110image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-08-02T15:15:12.641206image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-08-02T15:15:15.726169image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-08-02T15:15:19.053683image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-08-02T15:15:22.363072image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-08-02T15:15:26.486305image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-08-02T15:15:30.266641image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-08-02T15:15:33.604668image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-08-02T15:15:36.774804image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-08-02T15:15:39.956254image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-08-02T15:15:43.875471image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-08-02T15:15:47.520600image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-08-02T15:15:51.216559image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-08-02T15:15:55.199489image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-08-02T15:15:59.839925image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-08-02T15:15:01.346783image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-08-02T15:15:04.128903image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-08-02T15:15:07.000774image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-08-02T15:15:09.788193image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-08-02T15:15:12.847547image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-08-02T15:15:15.929115image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-08-02T15:15:19.193237image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-08-02T15:15:22.603700image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-08-02T15:15:26.673190image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-08-02T15:15:30.430967image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-08-02T15:15:33.767214image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-08-02T15:15:36.939592image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-08-02T15:15:40.125580image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-08-02T15:15:44.138138image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
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2023-08-02T15:16:02.609072image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-08-02T15:15:03.221882image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-08-02T15:15:06.051939image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-08-02T15:15:08.817162image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-08-02T15:15:11.850195image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-08-02T15:15:14.819298image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-08-02T15:15:18.193753image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-08-02T15:15:21.325060image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-08-02T15:15:25.236640image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-08-02T15:15:29.389586image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-08-02T15:15:32.728415image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-08-02T15:15:35.876799image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-08-02T15:15:39.022861image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-08-02T15:15:42.740094image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-08-02T15:15:46.433586image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-08-02T15:15:50.187091image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-08-02T15:15:53.779987image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-08-02T15:15:58.173350image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/

Correlations

2023-08-02T15:16:23.279785image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
encounter_idpatient_idhospital_idagebmiheighticu_idpre_icu_los_daysweightapache_2_diagnosisapache_3j_diagnosisgcs_motor_apacheheart_rate_apachemap_apacheresprate_apachetemp_apacheapache_4a_hospital_death_probapache_4a_icu_death_probelective_surgeryethnicitygendericu_admit_sourceicu_stay_typeicu_typeapache_post_operativearf_apachegcs_eyes_apachegcs_unable_apachegcs_verbal_apacheintubated_apacheventilated_apacheaidscirrhosisdiabetes_mellitushepatic_failureimmunosuppressionleukemialymphomasolid_tumor_with_metastasisapache_3j_bodysystemapache_2_bodysystemhospital_death
encounter_id1.000-0.010-0.004-0.004-0.002-0.006-0.001-0.000-0.0060.001-0.0000.008-0.0030.0020.0050.008-0.005-0.0060.0080.0000.0000.0030.0070.0000.0070.0060.0000.0090.0000.0000.0000.0000.0070.0100.0000.0040.0000.0070.0110.0000.0000.000
patient_id-0.0101.000-0.0070.0070.0010.003-0.002-0.0020.0020.0010.005-0.0010.004-0.0030.0000.0010.0040.0040.0000.0000.0000.0000.0000.0000.0000.0090.0000.0000.0040.0000.0030.0090.0040.0130.0000.0000.0070.0090.0050.0030.0020.009
hospital_id-0.004-0.0071.000-0.0100.0210.0290.0120.0090.0320.0080.021-0.004-0.009-0.004-0.029-0.045-0.026-0.0140.0830.1220.0240.0600.1450.1990.0770.0350.0670.0840.0820.1200.0870.0060.0300.0540.0290.0380.0100.0060.0120.0650.0600.039
age-0.0040.007-0.0101.000-0.102-0.124-0.0230.080-0.150-0.039-0.0940.007-0.142-0.0700.042-0.1190.3790.2980.1090.0750.0700.0640.0060.0420.0980.0390.0210.0060.0670.0490.0670.0340.0730.1180.0560.0530.0290.0250.0530.1220.1210.113
bmi-0.0020.0010.021-0.1021.000-0.0080.006-0.0050.8770.012-0.0330.040-0.0500.054-0.0160.037-0.074-0.0580.0630.0390.1220.0330.0100.0270.0590.0000.0170.0080.0470.0400.0770.0260.0000.1790.0000.0350.0120.0110.0460.0560.0490.057
height-0.0060.0030.029-0.124-0.0081.0000.021-0.0230.4300.0180.014-0.003-0.0260.049-0.0570.012-0.085-0.0660.0240.0630.7210.0170.0000.0220.0260.0120.0200.0000.0220.0230.0180.0070.0100.0060.0090.0000.0040.0050.0040.0310.0270.022
icu_id-0.001-0.0020.012-0.0230.0060.0211.0000.0510.015-0.028-0.024-0.0120.018-0.0040.0110.0070.0190.0130.1070.2460.0200.0840.2500.2670.1080.0490.0870.1350.1080.1030.1090.0110.0540.0680.0450.0560.0090.0200.0270.0590.0550.051
pre_icu_los_days-0.000-0.0020.0090.080-0.005-0.0230.0511.000-0.0160.1370.1660.0190.033-0.050-0.0140.0080.0560.0240.0240.0100.0000.0640.0410.0140.0190.0400.0150.0000.0140.0190.0300.0280.0160.0060.0150.0310.0440.0090.0180.0240.0200.044
weight-0.0060.0020.032-0.1500.8770.4300.015-0.0161.0000.018-0.0220.033-0.0550.070-0.0390.039-0.104-0.0820.0620.0550.3100.0340.0080.0280.0610.0100.0170.0050.0490.0450.0750.0170.0020.1610.0050.0280.0150.0080.0400.0490.0430.054
apache_2_diagnosis0.0010.0010.008-0.0390.0120.018-0.0280.1370.0181.0000.4940.045-0.0920.025-0.131-0.025-0.200-0.2300.7120.0300.0380.3700.0400.2030.7710.0370.0500.0270.0560.1320.1650.0100.0600.0510.0740.0490.0240.0170.0700.5280.5230.146
apache_3j_diagnosis-0.0000.0050.021-0.094-0.0330.014-0.0240.166-0.0220.4941.000-0.0150.027-0.039-0.107-0.002-0.214-0.1910.9000.0380.0920.4600.0580.2260.9880.0460.0800.0290.1110.2130.2400.0170.0890.0810.0990.0710.0310.0250.0590.7310.6240.140
gcs_motor_apache0.008-0.001-0.0040.0070.040-0.003-0.0120.0190.0330.045-0.0151.000-0.1140.036-0.0160.088-0.456-0.4880.0670.0190.0180.0380.0380.0530.0600.0000.5701.0000.4180.3770.4620.0000.0220.0260.0190.0190.0010.0000.0130.0860.0680.284
heart_rate_apache-0.0030.004-0.009-0.142-0.050-0.0260.0180.033-0.055-0.0920.027-0.1141.0000.0010.2000.0960.1890.2140.1240.0160.0420.0650.0320.0490.1090.0330.0720.0280.0700.0910.1280.0100.0140.0520.0100.0560.0220.0180.0420.0850.0610.166
map_apache0.002-0.003-0.004-0.0700.0540.049-0.004-0.0500.0700.025-0.0390.0360.0011.0000.0920.033-0.139-0.1570.0970.0440.0630.0580.0340.0420.1050.0520.0860.0270.0900.1730.1940.0000.0370.0310.0470.0150.0180.0120.0110.0730.0650.153
resprate_apache0.0050.000-0.0290.042-0.016-0.0570.011-0.014-0.039-0.131-0.107-0.0160.2000.0921.0000.0350.1550.1520.1590.0320.0620.0860.0270.0560.1610.0240.0310.0210.0430.0610.0840.0100.0110.0230.0070.0390.0210.0120.0190.0690.0570.101
temp_apache0.0080.001-0.045-0.1190.0370.0120.0070.0080.039-0.025-0.0020.0880.0960.0330.0351.000-0.122-0.1290.1120.0230.0190.0620.0310.0430.1180.0270.1740.0850.1290.2160.2440.0140.0320.0020.0370.0120.0170.0070.0150.0610.0480.241
apache_4a_hospital_death_prob-0.0050.004-0.0260.379-0.074-0.0850.0190.056-0.104-0.200-0.214-0.4560.189-0.1390.155-0.1221.0000.9430.1490.0150.0180.2060.2080.0450.1310.0320.3550.0850.2850.3890.4240.0120.0530.0150.0420.0480.0570.0280.0680.1090.0850.455
apache_4a_icu_death_prob-0.0060.004-0.0140.298-0.058-0.0660.0130.024-0.082-0.230-0.191-0.4880.214-0.1570.152-0.1290.9431.0000.1240.0120.0000.1970.0320.0360.1100.0260.3530.0880.2660.4040.4070.0130.0450.0170.0390.0260.0390.0100.0350.0830.0690.437
elective_surgery0.0080.0000.0830.1090.0630.0240.1070.0240.0620.7120.9000.0670.1240.0970.1590.1120.1490.1241.0000.0480.0300.8720.0640.3380.9080.0270.0410.0120.1130.1610.1460.0050.0310.0000.0340.0140.0170.0070.0150.3360.4440.093
ethnicity0.0000.0000.1220.0750.0390.0630.2460.0100.0550.0300.0380.0190.0160.0440.0320.0230.0150.0120.0481.0000.0300.0270.0390.0650.0460.1120.0260.0510.0200.0320.0240.0240.0560.0520.0510.0090.0040.0120.0150.0340.0330.012
gender0.0000.0000.0240.0700.1220.7210.0200.0000.3100.0380.0920.0180.0420.0630.0620.0190.0180.0000.0300.0301.0000.0340.0130.0650.0330.0090.0290.0070.0260.0320.0220.0130.0210.0110.0180.0000.0120.0010.0060.1120.0960.006
icu_admit_source0.0030.0000.0600.0640.0330.0170.0840.0640.0340.3700.4600.0380.0650.0580.0860.0620.2060.1970.8720.0270.0341.0000.1770.1750.9150.0390.0390.0240.0690.1790.1760.0100.0360.0230.0390.0330.0350.0160.0340.1930.2350.111
icu_stay_type0.0070.0000.1450.0060.0100.0000.2500.0410.0080.0400.0580.0380.0320.0340.0270.0310.2080.0320.0640.0390.0130.1771.0000.0760.0630.0160.0450.0210.0440.0160.0370.0080.0050.0130.0040.0060.0040.0040.0000.0550.0270.016
icu_type0.0000.0000.1990.0420.0270.0220.2670.0140.0280.2030.2260.0530.0490.0420.0560.0430.0450.0360.3380.0650.0650.1750.0761.0000.3380.0330.0710.0400.0730.1210.1150.0050.0330.0520.0360.0250.0110.0090.0220.2440.2300.051
apache_post_operative0.0070.0000.0770.0980.0590.0260.1080.0190.0610.7710.9880.0600.1090.1050.1610.1180.1310.1100.9080.0460.0330.9150.0630.3381.0000.0280.0440.0260.1230.1850.1780.0070.0320.0040.0340.0140.0130.0070.0120.3630.4660.084
arf_apache0.0060.0090.0350.0390.0000.0120.0490.0400.0100.0370.0460.0000.0330.0520.0240.0270.0320.0260.0270.1120.0090.0390.0160.0330.0281.0000.0080.0040.0120.0000.0000.0090.0210.1080.0130.0000.0110.0000.0070.0600.0600.027
gcs_eyes_apache0.0000.0000.0670.0210.0170.0200.0870.0150.0170.0500.0800.5700.0720.0860.0310.1740.3550.3530.0410.0260.0290.0390.0450.0710.0440.0081.0001.0000.4950.3960.4890.0080.0210.0240.0150.0180.0000.0070.0140.1120.0990.269
gcs_unable_apache0.0090.0000.0840.0060.0080.0000.1350.0000.0050.0270.0291.0000.0280.0270.0210.0850.0850.0880.0120.0510.0070.0240.0210.0400.0260.0041.0001.0001.0000.1180.1290.0000.0000.0130.0000.0080.0010.0020.0090.0200.0250.051
gcs_verbal_apache0.0000.0040.0820.0670.0470.0220.1080.0140.0490.0560.1110.4180.0700.0900.0430.1290.2850.2660.1130.0200.0260.0690.0440.0730.1230.0120.4951.0001.0000.4760.5880.0010.0220.0180.0150.0240.0080.0060.0180.1220.1050.242
intubated_apache0.0000.0000.1200.0490.0400.0230.1030.0190.0450.1320.2130.3770.0910.1730.0610.2160.3890.4040.1610.0320.0320.1790.0160.1210.1850.0000.3960.1180.4761.0000.6070.0050.0040.0000.0000.0070.0000.0020.0110.1100.2080.173
ventilated_apache0.0000.0030.0870.0670.0770.0180.1090.0300.0750.1650.2400.4620.1280.1940.0840.2440.4240.4070.1460.0240.0220.1760.0370.1150.1780.0000.4890.1290.5880.6071.0000.0040.0000.0100.0040.0000.0000.0000.0160.2170.2950.229
aids0.0000.0090.0060.0340.0260.0070.0110.0280.0170.0100.0170.0000.0100.0000.0100.0140.0120.0130.0050.0240.0130.0100.0080.0050.0070.0090.0080.0000.0010.0050.0041.0000.0090.0090.0000.0240.0000.0180.0000.0250.0080.002
cirrhosis0.0070.0040.0300.0730.0000.0100.0540.0160.0020.0600.0890.0220.0140.0370.0110.0320.0530.0450.0310.0560.0210.0360.0050.0330.0320.0210.0210.0000.0220.0040.0000.0091.0000.0150.5260.0000.0040.0000.0040.1290.1180.039
diabetes_mellitus0.0100.0130.0540.1180.1790.0060.0680.0060.1610.0510.0810.0260.0520.0310.0230.0020.0150.0170.0000.0520.0110.0230.0130.0520.0040.1080.0240.0130.0180.0000.0100.0090.0151.0000.0100.0000.0000.0000.0130.1110.1170.015
hepatic_failure0.0000.0000.0290.0560.0000.0090.0450.0150.0050.0740.0990.0190.0100.0470.0070.0370.0420.0390.0340.0510.0180.0390.0040.0360.0340.0130.0150.0000.0150.0000.0040.0000.5260.0101.0000.0000.0000.0000.0060.1480.1430.039
immunosuppression0.0040.0000.0380.0530.0350.0000.0560.0310.0280.0490.0710.0190.0560.0150.0390.0120.0480.0260.0140.0090.0000.0330.0060.0250.0140.0000.0180.0080.0240.0070.0000.0240.0000.0000.0001.0000.1340.1030.2690.1000.0580.044
leukemia0.0000.0070.0100.0290.0120.0040.0090.0440.0150.0240.0310.0010.0220.0180.0210.0170.0570.0390.0170.0040.0120.0350.0040.0110.0130.0110.0000.0010.0080.0000.0000.0000.0040.0000.0000.1341.0000.0300.0050.0620.0510.029
lymphoma0.0070.0090.0060.0250.0110.0050.0200.0090.0080.0170.0250.0000.0180.0120.0120.0070.0280.0100.0070.0120.0010.0160.0040.0090.0070.0000.0070.0020.0060.0020.0000.0180.0000.0000.0000.1030.0301.0000.0140.0390.0270.018
solid_tumor_with_metastasis0.0110.0050.0120.0530.0460.0040.0270.0180.0400.0700.0590.0130.0420.0110.0190.0150.0680.0350.0150.0150.0060.0340.0000.0220.0120.0070.0140.0090.0180.0110.0160.0000.0040.0130.0060.2690.0050.0141.0000.0790.0570.051
apache_3j_bodysystem0.0000.0030.0650.1220.0560.0310.0590.0240.0490.5280.7310.0860.0850.0730.0690.0610.1090.0830.3360.0340.1120.1930.0550.2440.3630.0600.1120.0200.1220.1100.2170.0250.1290.1110.1480.1000.0620.0390.0791.0000.9100.128
apache_2_bodysystem0.0000.0020.0600.1210.0490.0270.0550.0200.0430.5230.6240.0680.0610.0650.0570.0480.0850.0690.4440.0330.0960.2350.0270.2300.4660.0600.0990.0250.1050.2080.2950.0080.1180.1170.1430.0580.0510.0270.0570.9101.0000.111
hospital_death0.0000.0090.0390.1130.0570.0220.0510.0440.0540.1460.1400.2840.1660.1530.1010.2410.4550.4370.0930.0120.0060.1110.0160.0510.0840.0270.2690.0510.2420.1730.2290.0020.0390.0150.0390.0440.0290.0180.0510.1280.1111.000

Missing values

2023-08-02T15:16:03.112438image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
A simple visualization of nullity by column.
2023-08-02T15:16:04.321479image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Nullity matrix is a data-dense display which lets you quickly visually pick out patterns in data completion.
2023-08-02T15:16:06.253833image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
The correlation heatmap measures nullity correlation: how strongly the presence or absence of one variable affects the presence of another.

Sample

encounter_idpatient_idhospital_idagebmielective_surgeryethnicitygenderheighticu_admit_sourceicu_idicu_stay_typeicu_typepre_icu_los_daysweightapache_2_diagnosisapache_3j_diagnosisapache_post_operativearf_apachegcs_eyes_apachegcs_motor_apachegcs_unable_apachegcs_verbal_apacheheart_rate_apacheintubated_apachemap_apacheresprate_apachetemp_apacheventilated_apacheapache_4a_hospital_death_probapache_4a_icu_death_probaidscirrhosisdiabetes_mellitushepatic_failureimmunosuppressionleukemialymphomasolid_tumor_with_metastasisapache_3j_bodysystemapache_2_bodysystemUnnamed: 83hospital_death
0661542531211868.022.730CaucasianM180.3Floor92admitCTICU0.54166773.9113.0502.0100.03.06.00.04.0118.00.040.036.039.30.00.100.050.00.01.00.00.00.00.00.0SepsisCardiovascularNaN0
1114252593428177.027.420CaucasianF160.0Floor90admitMed-Surg ICU0.92777870.2108.0203.0100.01.03.00.01.0120.00.046.033.035.11.00.470.290.00.01.00.00.00.00.00.0RespiratoryRespiratoryNaN0
21197835077711825.031.950CaucasianF172.7Accident & Emergency93admitMed-Surg ICU0.00069495.3122.0703.0300.03.06.00.05.0102.00.068.037.036.70.00.000.000.00.00.00.00.00.00.00.0MetabolicMetabolicNaN0
3792674691811881.022.641CaucasianF165.1Operating Room / Recovery92admitCTICU0.00069461.7203.01206.0310.04.06.00.05.0114.01.060.04.034.81.00.040.030.00.00.00.00.00.00.00.0CardiovascularCardiovascularNaN0
492056343773319.0NaN0CaucasianM188.0Accident & Emergency91admitMed-Surg ICU0.073611NaN119.0601.0100.0NaNNaNNaNNaN60.00.0103.016.036.70.0NaNNaN0.00.00.00.00.00.00.00.0TraumaTraumaNaN0
533181744898367.027.560CaucasianM190.5Accident & Emergency95admitMed-Surg ICU0.000694100.0301.0403.0100.04.06.00.05.0113.00.0130.035.036.60.00.050.020.00.01.00.00.00.00.00.0NeurologicalNeurologicNaN0
682208495268359.057.450CaucasianF165.1Accident & Emergency95admitMed-Surg ICU0.000694156.6108.0203.0100.04.06.00.05.0133.01.0138.053.035.01.00.100.050.00.01.00.00.00.00.00.0RespiratoryRespiratoryNaN0
7120995501293370.0NaN0CaucasianM165.0Accident & Emergency91admitMed-Surg ICU0.002083NaN113.0501.0500.04.06.00.05.0120.00.060.028.036.61.00.110.060.00.00.00.01.00.00.00.0SepsisCardiovascularNaN0
8804711057711845.0NaN0CaucasianM170.2Other Hospital114admitCCU-CTICU0.009028NaN116.0103.0100.04.06.00.05.082.00.066.014.036.91.0NaNNaN0.00.00.00.00.00.00.00.0CardiovascularCardiovascularNaN1
9428719074911850.025.710NoneM175.3Accident & Emergency114admitCCU-CTICU0.06041779.0112.0107.0100.04.06.00.05.094.00.058.046.036.30.00.020.010.00.00.00.00.00.00.00.0CardiovascularCardiovascularNaN0
encounter_idpatient_idhospital_idagebmielective_surgeryethnicitygenderheighticu_admit_sourceicu_idicu_stay_typeicu_typepre_icu_los_daysweightapache_2_diagnosisapache_3j_diagnosisapache_post_operativearf_apachegcs_eyes_apachegcs_motor_apachegcs_unable_apachegcs_verbal_apacheheart_rate_apacheintubated_apachemap_apacheresprate_apachetemp_apacheventilated_apacheapache_4a_hospital_death_probapache_4a_icu_death_probaidscirrhosisdiabetes_mellitushepatic_failureimmunosuppressionleukemialymphomasolid_tumor_with_metastasisapache_3j_bodysystemapache_2_bodysystemUnnamed: 83hospital_death
91703655391280563053.045.9352030CaucasianM190.5Accident & Emergency921admitMed-Surg ICU0.097917166.7113.0501.0100.03.06.00.05.060.00.0133.034.036.90.00.030.010.00.01.00.00.00.00.00.0SepsisCardiovascularNaN0
9170496325776713038.032.9929230CaucasianM177.8Accident & Emergency927admitCardiac ICU0.015972104.3307.0704.0701.04.06.00.04.0106.00.094.014.036.80.00.010.010.00.00.00.00.00.00.00.0MetabolicMetabolicNaN0
91705111411784819567.028.8768430African AmericanM182.9Accident & Emergency908admitMed-Surg ICU0.21319496.6123.0702.0100.04.06.00.05.088.00.054.013.036.40.00.010.000.00.01.00.00.00.00.00.0MetabolicMetabolicNaN0
917061271385922312154.019.7704480Native AmericanM177.8Accident & Emergency925admitMed-Surg ICU0.02569462.5109.0108.0100.04.06.00.05.055.00.062.012.036.60.00.010.000.00.00.00.00.00.00.00.0CardiovascularCardiovascularNaN0
9170727634638183NaN33.9335180CaucasianF152.0Accident & Emergency909admitMed-Surg ICU-3.59305678.4NaNNaN0NaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNoneNoneNaN0
9170891592781083075.023.0602500CaucasianM177.8Floor927admitCardiac ICU0.29861172.9113.0501.0600.04.06.00.05.0115.00.048.09.036.61.00.120.050.00.01.00.00.00.00.01.0SepsisCardiovascularNaN0
91709661191348612156.047.1796710CaucasianF183.0Floor925admitMed-Surg ICU0.120139158.0113.0501.0500.04.06.00.05.0100.00.062.033.037.40.00.030.020.00.00.00.00.00.00.00.0SepsisCardiovascularNaN0
9171089815817919548.027.2369140CaucasianM170.2Accident & Emergency908admitMed-Surg ICU0.04652878.9123.0702.0100.03.06.00.04.0158.00.057.04.035.80.00.050.020.00.01.00.00.00.00.00.0MetabolicMetabolicNaN0
917113377612059866NaN23.2974810CaucasianF154.9Accident & Emergency922admitMed-Surg ICU0.08194455.9108.0203.0100.04.05.00.04.060.00.054.014.036.30.00.070.020.00.00.00.00.00.00.00.0RespiratoryRespiratoryNaN0
9171216715361210482.022.0312501CaucasianF160.0Operating Room / Recovery926admitMed-Surg ICU0.01805656.4304.01409.0210.04.06.00.01.0101.00.056.04.036.01.00.190.080.00.00.00.00.00.00.00.0GastrointestinalGastrointestinalNaN0